The feedforward connections, on the other hand, remained just fine.
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"Feedforward is especially suited to successful people,"Goldsmith said on his blog.
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Feedforward and feedback processes reinforce the perceived epistemic validity of narrativizing this way.
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Day 17: Ask your team and your close coworkers to give you some 'feedforward'...
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In feedforward interactions, a user's engagement with social media is inversely related to the user's ability to maintain distance from it as a consumer—the feedforward mechanic is a consumption of the human by social media, an autocannibalizng of the user as a communicative agent.
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Feedforward systems challenge the idea that platforms are simply receptacles for information, responding to user inputs.
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For example, you'll want to solicit "feedforward" in addition to feedback, so you get ideas for the future.
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So you've approached your team and asked them for feedback or feedforward, only to be met with blank stares.
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Taking place at the Guangdong Museum of Art in Guangzhou, China, the sixth edition of the Guangzhou Triennial is titled As We May Think: Feedforward.
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Meanwhile, the superficial frontal eye fields and lateral intraparietal area send raw sensory input to the deeper areas in the prefrontal cortex, in the form of bottom-up or feedforward signals.
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As a start, having a better grasp of what the brain's feedback connections are doing could lead to big steps in artificial intelligence research, which currently focuses more on feedforward signals and classification algorithms.
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But understanding how this loops works could ultimately help facilitate learning and cognitive processing by reducing interference between the feedforward and feedback signals and giving our brains a moment to breathe, according to the researchers.
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Though Ms. Spaninks, the Guangzhou Triennial's curator, said she was disappointed with the decision to pull the current works, she was hopeful that the remaining pieces in the show, which is titled "As We May Think: Feedforward," could still trigger debate about the future of science and technology.
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In Feed Forward, Mark B Hansen illustrates how this cognitive and algorithmic labor also exploits the activity of "feedforward" loops, which create a "precessual" rather than processual relationship between users and platforms in which the mechanisms that strengthen filter bubbles are not readily apprehended or perceived by a human subject.
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He believes that communicators who do not use feedforward will seem dogmatic. Richards wrote more in depth about the idea and importance of feedforward in communication in his book Speculative Instruments and has said that feedforward was his most important learned concept.
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Feedforward is not typically hyphenated in scholarly publications. Meckl and Seering of MIT and Book and Dickerson of Georgia Tech began the development of the concepts of Feedforward Control in the mid 1970s. The discipline of Feedforward Controls was well defined in many scholarly papers, articles and books by the late 1980s.
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Historically, the use of the term “feedforward” is found in works by Harold S. Black in US patent 1686792 (invented 17th March 1923) and D. M. MacKay as early as 1956. While MacKay's work is in the field of biological control theory, he speaks only of feedforward systems. MacKay does not mention “Feedforward Control” or allude to the discipline of “Feedforward Controls.” MacKay and other early writers who use the term “feedforward” are generally writing about theories of how human or animal brains work.. Black also has US patent 2102671 invented 2 August 1927 on the technique of feedback applied to electronic systems. The discipline of “feedforward controls” was largely developed by professors and graduate students at Georgia Tech, MIT, Stanford and Carnegie Mellon.
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The term feedforward can be contrasted with the more traditional term feedback as it relates to receiving information about performance. Feedback allows people to see how they are doing. Feedforward allows them to see how they could be performing; a future self. Feedforward is mainly used in education and therapy circles and mainly with children with disabilities.
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Feedforward is the provision of context of what one wants to communicate prior to that communication. In purposeful activity, feedforward creates an expectation which the actor anticipates. When expected experience occurs, this provides confirmatory feedback.
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A feedforward neural network is a type of artificial neural network.
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Doctoral dissertation, University of Auckland, New Zealand. Feedforward in behavioral and cognitive science may be defined as "images of adaptive future behavior, hitherto not mastered"; images capable of triggering that behavior in a challenging context. Feedforward is created by restructuring current component behaviors into what appears to be a new skill or level of performance. One concept of feedforward originated in behavioral science.
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Feedforward often works in concert with feedback loops for guidance systems in cybernetics or self-control in biology . Feedforward in management theory enables the prediction and control of organizational behavior. These concepts have developed during and since the 1990s.
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Baltimore; Woodbine House. Feedforward, on the other hand, is used with people who do not have a skill or when a new skill is emerging. Thus, feedforward is the method most often used in instructional or clinical settings. Because Feedforward involves new skills or behaviors performed by the viewer, it usually requires some degree of video editing to make it appear that the viewer is performing in an advanced manner.
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Feedforward control is a discipline within the field of automatic controls used in automation.
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Fast Artificial Neural Network (FANN) is cross-platform open-source programming library for developing multilayer feedforward Artificial Neural Networks.
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Feedforward is the concept of learning from the future concerning the desired behavior which the subject is encouraged to adopt.
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Related concepts have emerged in biology, cybernetics, and management sciences. The understanding of feedforward help the understanding of brain function and rapid learning. The concept contributed to research and development of video self modeling (VSM). The most productive advances in feedforward came from its association with videos that showed adaptive behavior (see Dowrick, 1983, pp.
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This task is much more difficult for neural networks. For simple feedforward neural networks, this task is not solveable because feedforward networks don't have any working memory. After all, including working memory into neural networks is a difficult task. There have been several approaches like PBWM or Long short-term memory which have working memory.
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When the Saturday Review asked Richards to write a piece for their "What I Have Learned" series, Richards (then aged 75) took the opportunity to expound upon his cybernetic concept of "feedforward". The Oxford English Dictionary records that Richards coined the term feedforward in 1951 at the Eighth Macy Conferences on cybernetics. In the event, the term extended the intellectual and critical influence of Richards to cybernetics which applied the term in a variety of contexts. Moreover, among Richards' students was Marshall McLuhan, who also applied and developed the term and the concept of feedforward.
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According to Richards, feedforward is the concept of anticipating the effect of one's words by acting as our own critic. It is thought to work in the opposite direction of feedback, though it works essentially towards the same goal: to clarify unclear concepts. Existing in all forms of communication, feedforward acts as a pretest that any writer can use to anticipate the impact of their words on their audience. According to Richards, feedforward allows the writer to then engage with their text to make necessary changes to create a better effect.
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Feedforward, Behavior and Cognitive Science is a method of teaching and learning that illustrates or indicates a desired future behavior or path to a goal. Feedforward provides information, images, etc. exclusively about what one could do right in the future, often in contrast to what one has done in the past. The feedforward method of teaching and learning is in contrast to its opposite, feedback, concerning human behavior because it focuses on learning in the future, whereas feedback uses information from a past event to provide reflection and the basis for behaving and thinking differently.
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Remembering the past and imagining the future: Common and distinct neural substrates during event construction and elaboration. Neuropsychologica, 45, 1363-1377. However, the links between these hot spots in the brain and feedforward learning have yet to be confirmed. Feedforward concepts have become established in at least four areas of science, and they continue to spread.
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In a feed forward network information always moves one direction; it never goes backwards. A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised.
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Other benefits of feedforward control include reduced wear and tear on equipment, lower maintenance costs, higher reliability and a substantial reduction in hysteresis. Feedforward control is often combined with feedback control to optimize performance.Oosting, K.W., Simulation of Control Strategies for a Two Degree-of-Freedom Lightweight Flexible Robotic Arm, Thesis, Georgia Institute of Technology, Dept. of Mechanical Engineering, 1987.
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Because the wavefront of activity spreads very fast, Victor Lamme and Pieter Roelfsema from the University of Amsterdam have proposed that this wave starts as a pure feedforward process (feedforward sweep): A cell first reached by the wavefront has to pass on its activity before being able to integrate feedback from other cells. Lamme and Roelfsema assume that this kind of feedforward processing is not sufficient to generate visual awareness of the stimulus: For this, neuronal feedback and recurrent processing loops are required that link widespread neuronal networks. According to rapid-chase theory, both primes and targets elicit feedforward sweeps that traverse the visuomotor system in rapid succession until they reach motor areas of the brain. There, motor processes are elicited automatically and without the need for a conscious representation.
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The benefits of feedforward control are significant and can often justify the extra cost, time and effort required to implement the technology. Control accuracy can often be improved by as much as an order of magnitude if the mathematical model is of sufficient quality and implementation of the feedforward control law is well thought out. Energy consumption by the feedforward control system and its driver is typically substantially lower than with other controls. Stability is enhanced such that the controlled device can be built of lower cost, lighter weight, springier materials while still being highly accurate and able to operate at high speeds.
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The above description applies well to feedforward inputs to neurons, which provide inputs from either sensory nerves or lower-level regions in the brain. About 90% of interneural connections are, however, not feedforward but predictive (or modulatory, or attentional) in nature. These connections receive inputs mainly from nearby cells in the same layer as the receiving cell, and also from distant connections which are fed through Layer 1. The dendrites which receive these inputs are quite distant from the cell body, and therefore they exhibit different electrical and signal-processing behaviour compared with the proximal (or feedforward) dendrites described above.
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A multilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The term MLP is used ambiguously, sometimes loosely to any feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons (with threshold activation); see . Multilayer perceptrons are sometimes colloquially referred to as "vanilla" neural networks, especially when they have a single hidden layer.Hastie, Trevor.
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This is an example of feedforward stimulation as glycolysis is accelerated when glucose is abundant. PFK activity is reduced through repression of synthesis by glucagon.
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However, recurrent feedback is difficult to determine using electrophysiology, and the potential mechanisms at play are not as well studied as feedforward or lateral connections.
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The feedforward comb filter is one of the simplest finite impulse response filters. Its response is simply the initial impulse with a second impulse after the delay.
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In: Advances in Cognitive Psychology. Nr. 3, 2007, p. 449–465. According to rapid-chase theory, response priming effects are independent of visual awareness because they are carried by rapid feedforward processes whereas the emergence of a conscious representation of the stimuli is dependent on slower, recurrent processes. The most important prediction of rapid-chase theory is that the feedforward sweeps of prime and target signals should occur in strict sequence.
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An auditory–motor interaction may be loosely defined as any engagement of or communication between the two systems. Two classes of auditory-motor interaction are "feedforward" and "feedback". In feedforward interactions, it is the auditory system that predominately influences the motor output, often in a predictive way. An example is the phenomenon of tapping to the beat, where the listener anticipates the rhythmic accents in a piece of music.
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The feedforward neural network was the first and simplest type. In this network the information moves only from the input layer directly through any hidden layers to the output layer without cycles/loops. Feedforward networks can be constructed with various types of units, such as binary McCulloch–Pitts neurons, the simplest of which is the perceptron. Continuous neurons, frequently with sigmoidal activation, are used in the context of backpropagation.
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Feedforward has been applied to the context of management. It often involves giving a pre-feedback to a person or an organization from which you are expecting a feedback.
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Emotional responses can also trigger GI response such as the butterflies in the stomach feeling when nervous. The feedforward and emotional reflexes of the GI tract are considered cephalic reflexes.
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To control the transfer functions that include these systems some methods such as internal model controller (IMC), generalized Smith's predictor (GSP) and parallel feedforward control with derivative (PFCD) are proposed.
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92–101, 2010. . The vanishing gradient problem affects many-layered feedforward networks that used backpropagation and also recurrent neural networks (RNNs).S. Hochreiter., "Untersuchungen zu dynamischen neuronalen Netzen," Diploma thesis.
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Transit time effects are important at these frequencies, so feedback is not normally usable and for performance critical applications alternative linearisation techniques have to be used such as degeneration and feedforward.
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Evolutionary multi-task learning for modular training of feedforward neural networks. In International Conference on Neural Information Processing (pp. 37-46). Springer, Cham.Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014).
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Ultra-rapid visual categorization is a model proposing an automatic feedforward mechanism that forms high-level object representations in parallel without focused attention. In this model, the mechanism cannot be sped up by training. Evidence for a feedforward mechanism can be found in studies that have shown that many neurons are already highly selective at the beginning of a visual response, thus suggesting that feedback mechanisms are not required for response selectivity to increase.Fabre-Thorpe, M., Delorme, A., Marlot, C., & Thorpe, S. (2001).
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Researchers have begun implementing controllers in into robots to control for stiffness. One such model adjusts for stiffness during robotic locomotion by virtually cocontracting antagonistic muscles about the robot's joints to modulate stiffness while a central pattern generator (CPG) controls the robot's locomotion. Other models of the neural modulation of stiffness include a feedforward model of stiffness adjustment. These models of neural control support the idea that humans use a feedforward mechanism of stiffness selection in anticipation of the required stiffness needed to accomplish a given task.
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The comparative simplicity and regularity of the cerebellar anatomy led to an early hope that it might imply a similar simplicity of computational function, as expressed in one of the first books on cerebellar electrophysiology, The Cerebellum as a Neuronal Machine by John C. Eccles, Masao Ito, and János Szentágothai. Although a full understanding of cerebellar function has remained elusive, at least four principles have been identified as important: (1) feedforward processing, (2) divergence and convergence, (3) modularity, and (4) plasticity. # Feedforward processing: The cerebellum differs from most other parts of the brain (especially the cerebral cortex) in that the signal processing is almost entirely feedforward—that is, signals move unidirectionally through the system from input to output, with very little recurrent internal transmission. The small amount of recurrence that does exist consists of mutual inhibition; there are no mutually excitatory circuits.
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Speech motor learning is an important part of the linguistic development of infants as they learn to use their mouths to articulate the various speech sounds in language. Speech production requires feedforward and feedback control pathways, in which the feedforward pathway directly controls the movements of the articulators (namely the lips, teeth, tongue and the other speech organs). Typical tongue movements have been generated as a training set using major muscle combinations, and these muscle combinations are used as a basis for articulating a set of whole proto-vocalic tongue babbling movements in infants.
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More generally, any directed acyclic graph may be used for a feedforward network, with some nodes (with no parents) designated as inputs, and some nodes (with no children) designated as outputs. These can be viewed as multilayer networks where some edges skip layers, either counting layers backwards from the outputs or forwards from the inputs. Various activation functions can be used, and there can be relations between weights, as in convolutional neural networks. Examples of other feedforward networks include radial basis function networks, which use a different activation function.
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The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965. These networks are trained one layer at a time. Ivakhnenko's 1971 paper describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning.
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Video self modeling applications with students with autism spectrum disorder in a small private school setting. Focus on Autism and Other Developmental Disabilities, 20, 52-63.Dowrick, P. W., Kim-Rupnow, W. S., & Power, T. J. (2006). Video feedforward for reading.
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MyoD is inhibited by cyclin dependent kinases (CDKs). CDKs are in turn inhibited by p21. Thus MyoD enhances its own activity in the cell in a feedforward manner. Sustained MyoD expression is necessary for retaining the expression of muscle-related genes.
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Sometimes multi-layer perceptron is used loosely to refer to any feedforward neural network, while in other cases it is restricted to specific ones (e.g., with specific activation functions, or with fully connected layers, or trained by the perceptron algorithm).
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Feed forward is a type of element or pathway within a control system. Feedforward control uses measurement of a disturbance input to control a manipulated input. This differs from feedback, which uses measurement of any output to control a manipulated input.
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The mathematical model of the plant (machine, process or organism) used by the feedforward control system may be created and input by a control engineer or it may be learned by the control system. Control systems capable of learning and/or adapting their mathematical model have become more practical as microprocessor speeds have increased. The discipline of modern feedforward control was itself made possible by the invention of microprocessors.Alberts, T.E., Sangveraphunsiri, V. and Book, Wayne J., Optimal Control of a Flexible Manipulator Arm: Volume I, Dynamic Modeling, MHRC Technical Report, MHRC- TR-85-06, Georgia Inst, of Technology, 1985.
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Bullier, J.: Integrated model of visual processing. In: Brain Research Reviews, Nr. 36, 2001, p. 96-107.Lamme, V. A. F., & Roelfsema, P. R.: The distinct modes of vision offered by feedforward and recurrent processing. In: Trends in Neurosciences, Nr. 23, 2000, p. 571-579.
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This can be very time-consuming; however, it has been used effectively for eating problems. A child can be filmed during several lunch periods and best examples of appropriate eating (such as putting food to mouth) can be extracted and combined into a feedforward movie.
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The difference is that the I1-FFL can speed-up the response of any gene and not necessarily a transcription factor gene. An additional function was assigned to the I1-FFL network motif: it was shown both theoretically and experimentally that the I1-FFL can generate non-monotonic input function in both a synthetic and native systems. Finally, expression units that incorporate incoherent feedforward control of the gene product provide adaptation to the amount of DNA template and can be superior to simple combinations of constitutive promoters. Feedforward regulation displayed better adaptation than negative feedback, and circuits based on RNA interference were the most robust to variation in DNA template amounts.
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The comparator-model, also known as the forward model, is an elaboration of theory of misattributed inner speech. This theory relies on a model involved in inner speech known as the forward model. Specifically, the comparator-model of thought insertion describes processing of movement-related sensory feedback involving a parietal-cerebellar network as subject to feedforward inhibition during voluntary movements and this is thought to contribute to the subject feeling as though thoughts are inserted into his or her mind. It has been proposed that the loss of sense of agency results from a disruption of feedforward inhibition of somatosensory processing for self-generated movements.
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In isolation, feedback is the least effective form of instruction, according to US Department of Defense studies in the 1980s. Feedforward was coined in 1976 by Peter W. Dowrick in his dissertation.Dowrick, P. W. (1976). Self modelling: A videotape training technique for disturbed and disabled children.
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Journal of Special Education, 39, 194-207. and on the web (e.g., in sport). The evidence for ultra-rapid learning, built from component behaviors that are reconfigured to appear as new skills, indicates the feedforward self model mechanism existing in the brain to control our future behavior.
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Sertoli cells are required for male sexual development. During male development, the gene SRY activates SOX9, which then activates and forms a feedforward loop with FGF9. Sertoli cell proliferation and differentiation is mainly activated by FGF9. The absence of FGF9 tends to cause a female to develop.
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The standard method is called "backpropagation through time" or BPTT, a generalization of back-propagation for feedforward networks.David E. Rumelhart; Geoffrey E. Hinton; Ronald J. Williams. Learning Internal Representations by Error Propagation. A more computationally expensive online variant is called "Real-Time Recurrent Learning" or RTRL.
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Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992.Martin Riedmiller und Heinrich Braun: Rprop - A Fast Adaptive Learning Algorithm.
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Feedback and feedforward regulation maintains the levels of bioactive GAs in plants. Levels of AtGA20ox1 and AtGA3ox1 expression are increased in a GA deficient environment, and decreased after the addition of bioactive GAs, Conversely, expression of AtGA2ox1 and AtGA2ox2, GA deactivation genes, is increased with addition of GA.
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Unless the system includes a means to detect a disturbance or receive an input and process that input through the mathematical model to determine the required modification to the control action, it is not true feedforward control.Book, W.J., Modeling, Design and Control of Flexible Manipulator Arms, PhD. Thesis, MIT, Dept. of Mech. Eng.
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Thus, in total, the activation pattern of the motor map is not only influenced by a specific feedforward command learned for a speech unit (and generated by the synaptic projection from the speech sound map) but also by a feedback command generated at the level of the sensory error maps (see Fig. 4).
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Feedforward control requires integration of the mathematical model into the control algorithm such that it is used to determine the control actions based on what is known about the state of the system being controlled. In the case of control for a lightweight, flexible robotic arm, this could be as simple as compensating between when the robot arm is carrying a payload and when it is not. The target joint angles are adjusted to place the payload in the desired position based on knowing the deflections in the arm from the mathematical model's interpretation of the disturbance caused by the payload. Systems that plan actions and then pass the plan to a different system for execution do not satisfy the above definition of feedforward control.
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The `nn` package is used for building neural networks. It is divided into modular objects that share a common `Module` interface. Modules have a `forward()` and `backward()` method that allow them to feedforward and backpropagate, respectively. Modules can be joined together using module composites, like `Sequential`, `Parallel` and `Concat` to create complex task-tailored graphs.
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Modeling design depends on whether it is artificial neuron or biological neuron of neuronal model. Type I or Type II choice needs to be made for the firing mode. Signaling in neurons could be rate-based neurons, spiking response neurons, or deep-brain stimulated. The network can be designed as feedforward or recurrent type.
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In 2004 Brixey and two DXARTS doctoral students Bret Battey and Ian Ingram received an Editors Choice Award in Popular Science Magazine's "World Design Challenge". The winning entry was awarded for novel use of feedforward ultrasound technology used to produce wide-field active noise cancellation in underwater environments specifically to protect endangered marine mammals.
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Schmidt, T., & Schmidt, F.: Processing of natural images is feedforward: A simple behavioral test. In: Attention, Perception, & Psychophysics, Nr. 71, 2009, p. 594-606. from measurements of response force,Mattler, U.: Flanker effects on motor output and the late-level response activation hypothesis. In: The Quarterly Journal of Experimental Psychology, Nr. 58A, 2005, p. 577-601.
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Allosteric regulations are a natural example of control loops, such as feedback from downstream products or feedforward from upstream substrates. Long-range allostery is especially important in cell signaling. Allosteric regulation is also particularly important in the cell's ability to adjust enzyme activity. The term allostery comes from the Ancient Greek allos (), "other", and stereos (), "solid (object)".
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Various studies have demonstrated this idea that visual processing relies on both feedforward and feedback systems (Jensen et al., 2015; Layher et al., 2014; Lee, 2002). Various studies that recorded from early visual neurons in macaque monkeys found evidence that early visual neurons are sensitive to features both within their receptive fields and the global context of a scene.
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Comb filters exist in two different forms, feedforward and feedback; the names refer to the direction in which signals are delayed before they are added to the input. Comb filters may be implemented in discrete time or continuous time; this article will focus on discrete-time implementations; the properties of the continuous-time comb filter are very similar.
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VanRullen (2006) ran simulations showing that the feedforward propagation of one wave of spikes through high- level neurons, generated in response to a stimulus, could be enough for crude recognition and categorization that occurs in 150 ms or less.VanRullen, R. (2007). The power of the feed-forward sweep. Advances in Cognitive Psychology, 3(1), 167-176.
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The biological mechanisms behind surround suppression are not known. The differences between lateral, feedforward, and recurrent connections Several theories have been proposed for the biological basis of this effect. Based on the diversity of the stimulus characteristics that cause this effect and the variety of responses that are generated, it seems that many mechanisms may be at play.
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In machine learning, backpropagation (backprop,, "The back-propagation algorithm (Rumelhart et al., 1986a), often simply called backprop, ..." BP) is a widely used algorithm in training feedforward neural networks for supervised learning. Generalizations of backpropagation exists for other artificial neural networks (ANNs), and for functions generally. These classes of algorithms are all referred to generically as "backpropagation".
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A regulatory feedback network makes inferences using negative feedback.Achler T., Omar C., Amir E., "Shedding Weights: More With Less", IEEE Proc. International Joint Conference on Neural Networks, 2008 The feedback is used to find the optimal activation of units. It is most similar to a non- parametric method but is different from K-nearest neighbor in that it mathematically emulates feedforward networks.
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In: H. C. Muffley/D. Bootzin (Eds.), Biomechanics, Plenum, pp. 149–180 A pure feed-forward system is different from a homeostatic control system, which has the function of keeping the body's internal environment 'steady' or in a 'prolonged steady state of readiness.' A homeostatic control system relies mainly on feedback (especially negative), in addition to the feedforward elements of the system.
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Together, these findings indicate abnormal processing of internally generated sensory experiences, coupled with abnormal emotional processing, results in hallucinations. One proposed model involves a failure of feedforward networks from sensory cortices to the inferior frontal cortex, which normally cancel out sensory cortex activity during internally generated speech. The resulting disruption in expected and perceived speech is thought to produce lucid hallucinatory experiences.
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Richards subsequently continued: "The point is that feedforward is a needed prescription or plan for a feedback, to which the actual feedback may or may not confirm." The term was picked up and developed by the cybernetics community. This enabled the word to then be introduced to more specific fields such as control systems, management, neural networks, cognitive studies and behavioural science.
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The duality of structure is essentially a feedback–feedforward process whereby agents and structures mutually enact social systems, and social systems in turn become part of that duality. Structuration thus recognizes a social cycle. In examining social systems, structuration theory examines structure, modality, and interaction. The "modality" (discussed below) of a structural system is the means by which structures are translated into actions.
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Two datasets are linearly separable if their convex hulls do not intersect. The method may be formulated as a feedforward neural network with weights that are trained via linear programming. Comparisons between neural networks trained with the MSM versus backpropagation show MSM is better able to classify data.Neural Network Training via Linear Programing, Advances in Optimization and Parallel Computing, 1992, p.
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Bottom-up processing refers to the visual system's ability to use the incoming visual information, flowing in a unidirectional path from the retina to higher cortical areas. Top-down processing refers to the use of prior knowledge and context to process visual information and change the information conveyed by neurons, altering the way they are tuned to a stimulus. All areas of the visual pathway except for the retina are able to be influenced by top-down processing. There is a traditional view that visual processing follows a feedforward system where there is a one-way process by which light is sent from the retina to higher cortical areas, however, there is increasing evidence that visual pathways operate bidirectionally, with both feedforward and feedback mechanisms in place that transmit information to and from lower and higher cortical areas.
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Hallucinations are associated with structural and functional abnormalities in primary and secondary sensory cortices. Reduced grey matter in regions of the superior temporal gyrus/middle temporal gyrus, including Broca's area, is associated with auditory hallucinations as a trait, while acute hallucinations are associated with increased activity in the same regions along with the hippocampus, parahippocampus, and the right hemispheric homologue of Broca's area in the inferior frontal gyrus. Grey and white matter abnormalities in visual regions are associated with visual hallucinations in diseases such as Alzheimer's disease, further supporting the notion of dysfunction in sensory regions underlying hallucinations. One proposed model of hallucinations posits that overactivity in sensory regions, which is normally attributed to internal sources via feedforward networks to the inferior frontal gyrus, is interpreted as originating externally due to abnormal connectivity or functionality of the feedforward network.
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WaveNet is a type of feedforward neural network known as a deep convolutional neural network (CNN). In WaveNet, the CNN takes a raw signal as an input and synthesises an output one sample at a time. It does so by sampling from a softmax (i.e. categorical) distribution of a signal value that is encoded using μ-law companding transformation and quantized to 256 possible values.
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The associative neural network (ASNN) is an extension of committee of machines that combines multiple feedforward neural networks and the k-nearest neighbor technique. It uses the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN. This corrects the Bias of the neural network ensemble. An associative neural network has a memory that can coincide with the training set.
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Studies also show that the variation of limb stiffness is important when hopping, and that different people may control this stiffness variation in different ways. One study showed that adults had more feedforward neural control, muscle reflexes, and higher relative leg stiffness than their juvenile counterparts when performing a hopping task. This indicates that the control of stiffness may vary from person to person.
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Pao was given the Richard and Joy Dorf Professorship in 2009. She was named an IEEE Fellow in 2012 "for contributions to feedforward and feedback control systems", and named a fellow of the International Federation of Automatic Control in 2014. In 2017 the American Automatic Control Council gave her their Control Engineering Practice Award "for pioneering applications of advanced control to wind turbines and wind farms".
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They can be pooling, where a group of neurons in one layer connect to a single neuron in the next layer, thereby reducing the number of neurons in that layer. Neurons with only such connections form a directed acyclic graph and are known as feedforward networks. Alternatively, networks that allow connections between neurons in the same or previous layers are known as recurrent networks'.
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His work relies on control theory; a means to evaluate how systems behave with a series of inputs and desired outputs. This may include nanoscale motion control, vehicle systems dynamics and energy management (including heating, ventilation, and air conditioning systems). He has studied advances in Iterative Learning Control (ILC). Alleyne has created several high precision algorithms that include design rules for ILC feedforward trajectories.
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"Adaptability" is the ability of a living system to respond to needs, dangers, or changes. It is distinguished from improvisation because the response is timely and does not involve a change of the program. Adaptability occurs from a molecular level to a behavioral level through feedback and feedforward systems. For example, an animal seeing a predator will respond to the danger with hormonal changes and escape behavior.
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The function of this couple is to manifest figurative attributes of the personality, like goals or ideology, operatively consequently influencing behaviour. This instrumental nature occurs through feedforward processes such that personality attributes can be processed for operative action. Where there are issues in doing this, feedback processes create imperatives for adjustment. This is like having a goal, and finding that it cannot be implemented, thereby having to reconsider the goal.
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DNNs are typically feedforward networks in which data flows from the input layer to the output layer without looping back. At first, the DNN creates a map of virtual neurons and assigns random numerical values, or "weights", to connections between them. The weights and inputs are multiplied and return an output between 0 and 1. If the network did not accurately recognize a particular pattern, an algorithm would adjust the weights.
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Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled. Both finite impulse and infinite impulse recurrent networks can have additional stored states, and the storage can be under direct control by the neural network.
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FGF9 has also been shown to play a vital role in male sex development. FGF9’s role in sex determination begins with its expression in the bi-potent gonads for both females and males. Once activated by SOX9, it is responsible for forming a feedforward loop with Sox9, increasing the levels of both genes. It forms a positive feedback loop upregulating SOX9, while simultaneously inactivating the female Wnt4 signaling pathway.
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In his book, Seeing is Believing, Tom Buggey lists three major ways video footage can be collected and compiled into a feedforward video: # Imitation – Particularly useful with language skills. Children are prompted to say words or phrases. Words can be new or rarely used and phrases and sentences can be longer or more complex than presently used. Individual words can even be extracted from videos using video editing software and joined into sentences.
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A time delay neural network (TDNN) is a feedforward architecture for sequential data that recognizes features independent of sequence position. In order to achieve time-shift invariance, delays are added to the input so that multiple data points (points in time) are analyzed together. It usually forms part of a larger pattern recognition system. It has been implemented using a perceptron network whose connection weights were trained with back propagation (supervised learning).
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In physiology, feed-forward control is exemplified by the normal anticipatory regulation of heartbeat in advance of actual physical exertion by the central autonomic network. Feed-forward control can be likened to learned anticipatory responses to known cues (predictive coding). Feedback regulation of the heartbeat provides further adaptiveness to the running eventualities of physical exertion. Feedforward systems are also found in biological control of other variables by many regions of animals brains.
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Even in the case of biological feedforward systems, such as in the human brain, knowledge or a mental model of the plant (body) can be considered to be mathematical as the model is characterized by limits, rhythms, mechanics and patterns.MacKay, D. M. (1966): "Cerebral organization and the conscious control of action". In: J. C. Eccles (Ed.), Brain and conscious experience, Springer, pp. 422–440Greene, P. H. (1969): "Seeking mathematical models of skilled actions".
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Ciresan and colleagues (2010) showed that despite the vanishing gradient problem, GPUs make backpropagation feasible for many-layered feedforward neural networks.Dominik Scherer, Andreas C. Müller, and Sven Behnke: "Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition," In 20th International Conference Artificial Neural Networks (ICANN), pp. 92–101, 2010. . Between 2009 and 2012, ANNs began winning prizes in ANN contests, approaching human level performance on various tasks, initially in pattern recognition and machine learning.
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The TRACE model is a connectionist network with an input layer and three processing layers: pseudo-spectra (feature), phoneme and word. Figure 2 shows a schematic diagram of TRACE. There are three types of connectivity: (1) feedforward excitatory connections from input to features, features to phonemes, and phonemes to words; (2) lateral (i.e., within layer) inhibitory connections at the feature, phoneme and word layers; and (3) top-down feedback excitatory connections from words to phonemes.
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CDO contains a unique internal cofactor created by intramolecular thioether formation between Cys93 and Tyr157, which is postulated to participate in catalysis. When the protein was first isolated, two bands on agarose gel were observed, corresponding to the cofactor- containing protein and the unlinked "immature" protein, respectively. Crosslinking increases efficiency of CDO ten-fold and is regulated by levels of cysteine, an unusual example of protein cofactor formation mediated by substrate (feedforward activation).
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These findings could be confirmed with different methods and different types of stimuli. Because rapid-chase theory views response priming as a feedforward process, it maintains that priming effects occur before recurrent and feedback activity take part in stimulus processing. The theory therefore leads to the controversial thesis that response priming effects are a measure of preconscious processing of visual stimuli, which may be qualitatively different from the way those stimuli are finally represented in visual awareness.
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Video self-modeling (VSM) is a form of observational learning in which individuals observe themselves performing a behavior successfully on video, and then imitate the targeted behavior. VSM allows individuals to view themselves being successful, acting appropriately, or performing new tasks. Peter Dowrick, a key researcher in the development of self-modeling, described two forms of VSM, feedforward and self-review. Self-review involves someone with a relatively well developed skill watching examples of best performance.
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The first general, working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Alexey Ivakhnenko and Lapa in 1967. A 1971 paper described a deep network with eight layers trained by the group method of data handling. Other deep learning working architectures, specifically those built for computer vision, began with the Neocognitron introduced by Kunihiko Fukushima in 1980. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986,Rina Dechter (1986).
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Long reflexes to the digestive system involve a sensory neuron sending information to the brain, which integrates the signal and then sends messages to the digestive system. While in some situations, the sensory information comes from the GI tract itself; in others, information is received from sources other than the GI tract. When the latter situation occurs, these reflexes are called feedforward reflexes. This type of reflex includes reactions to food or danger triggering effects in the GI tract.
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The entorhinal cortex also projects directly to CA3, suggesting that the mossy fiber pathway may be functionally similar to the perforant pathway although microcircuits within the dentate gyrus give the mossy fiber pathway a more modulatory role. Projections to the dentate hilus are excitatory by nature and oppose the inhibitory effects of interneurons on hilar mossy cells. The result is an excitatory feedforward loop on mossy cells as a result of activation by the entorhinal cortex.
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A probabilistic neural network (PNN) is a four-layer feedforward neural network. The layers are Input, hidden, pattern/summation and output. In the PNN algorithm, the parent probability distribution function (PDF) of each class is approximated by a Parzen window and a non-parametric function. Then, using PDF of each class, the class probability of a new input is estimated and Bayes’ rule is employed to allocate it to the class with the highest posterior probability.
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Typically, this activity is understood to reflect feedforward connections between distinct brain regions, in contrast to alpha wave feedback across the same regions. Gamma oscillations have also been shown to correlate with the firing of single neurons, mostly inhibitory neurons, during all states of the wake-sleep cycle. Gamma wave activity is most prominent during alert, attentive wakefulness. However, the mechanisms and substrates by which gamma activity may help to generate different states of consciousness remain unknown.
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Deep Neural Networks and Denoising Autoencoders are also under investigation. A deep feedforward neural network (DNN) is an artificial neural network with multiple hidden layers of units between the input and output layers. Similar to shallow neural networks, DNNs can model complex non-linear relationships. DNN architectures generate compositional models, where extra layers enable composition of features from lower layers, giving a huge learning capacity and thus the potential of modeling complex patterns of speech data.
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Two other monkey study used electrophysiology to find different frequencies that are associated with feedforward and feedback processing in monkeys (Orban, 2008; Schenden & Ganis, 2005). Studies with monkeys have also shown that neurons in higher level visual areas are selective to certain stimuli. One study that used single unit recordings in macaque monkeys found that neurons in middle temporal visual area, also known as area MT or V5, were highly selective for both direction and speed (Maunsell & Van Essen, 1983).
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Can vary from microseconds (MEMS and magnetics mirrors) to tens of seconds for thermally controlled DM's. Hysteresis and creep are nonlinear actuation effects that decrease the precision of the response of the deformable mirror. For different concepts, the hysteresis can vary from zero (electrostatically-actuated mirrors) to tens of percent for mirrors with piezoelectric actuators. Hysteresis is a residual positional error from previous actuator position commands, and limits the mirror ability to work in a feedforward mode, outside of a feedback loop.
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However the network is restricted to the single style in which it has been trained. In a work by Chen Dongdong et al. they explored the fusion of optical flow information into feedforward networks in order to improve the temporal coherence of the output. Most recently, feature transform based NST methods have been explored for fast stylization that are not coupled to single specific style and enable user-controllable blending of styles, for example the Whitening and Coloring Transform (WCT).
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In Machine Learning and Computer Vision, M-Theory is a learning framework inspired by feed-forward processing in the ventral stream of visual cortex and originally developed for recognition and classification of objects in visual scenes. M-Theory was later applied to other areas, such as speech recognition. On certain image recognition tasks, algorithms based on a specific instantiation of M-Theory, HMAX, achieved human-level performance.Serre T., Oliva A., Poggio T. (2007) A feedforward architecture accounts for rapid categorization.
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While auditory feedback is most important during speech acquisition, it may be activated less if the model has learned a proper feedforward motor command for each speech unit. But it has been shown that auditory feedback needs to be strongly coactivated in the case of auditory perturbation (e.g. shifting a formant frequency, Tourville et al. 2005).Tourville J, Guenther F, Ghosh S, Reilly K, Bohland J, Nieto-Castanon A (2005) Effects of acoustic and articulatory perturbation on cortical activity during speech production.
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Whalley, WB, (2013) Teaching with assessment, feedback and feedforward: using 'preflights' to assist student achievement, in For the love of learning, Innovations from outstanding university teachers, Bilham, T (Ed), Palgrave Macmillan, 97-102. . JiTT activities also take into account motivational factors governing student behavior. Motivational belief theorists take the constructivist position that "the process of conceptual change is influenced by personal, motivational, social, and historical processes, thereby advocating a hot model of individual conceptual change".Pintrich, PR, Marx, RW, Boyle, RA (1993).
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The promoter based genetic algorithm (PBGA) is a genetic algorithm for neuroevolution developed by F. Bellas and R.J. Duro in the Integrated Group for Engineering Research (GII) at the University of Coruña, in Spain. It evolves variable size feedforward artificial neural networks (ANN) that are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines if that particular unit will be expressed or not.
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FBP is the most significant source of regulation because it comes from within the glycolysis pathway. FBP is a glycolytic intermediate produced from the phosphorylation of fructose 6-phosphate. FBP binds to the allosteric binding site on domain C of pyruvate kinase and changes the conformation of the enzyme, causing the activation of pyruvate kinase activity. As an intermediate present within the glycolytic pathway, FBP provides feedforward stimulation because the higher the concentration of FBP, the greater the allosteric activation and magnitude of pyruvate kinase activity.
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A Bayesian Confidence Propagation Neural Network (BCPNN) is an artificial neural network inspired by Bayes' theorem: node activations represent probability ("confidence") in the presence of input features or categories, synaptic weights are based on estimated correlations and the spread of activation corresponds to calculating posteriori probabilities. It was originally proposed by Anders Lansner and Örjan Ekeberg at KTH. The basic network is a feedforward neural network with continuous activation. This can be extended to include spiking units and hypercolumns, representing mutually exclusive or interval coded features.
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Such a solution was shown to outperform ESNs with trainable (finite) sets of weights in several benchmarks. Some publicly available implementations of ESNs are: (i) aureservoir: an efficient C++ library for various kinds of echo state networks with python/numpy bindings; and (ii) Matlab code: an efficient matlab for an echo state network. The Echo State Network (ESN) belongs to the Recurrent Neural Network (RNN) family and provide their architecture and supervised learning principle. Unlike Feedforward Neural Networks, Recurrent Neural Networks are dynamic systems and not functions.
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Most speech recognition researchers who understood such barriers hence subsequently moved away from neural nets to pursue generative modeling approaches until the recent resurgence of deep learning starting around 2009–2010 that had overcome all these difficulties. Hinton et al. and Deng et al. reviewed part of this recent history about how their collaboration with each other and then with colleagues across four groups (University of Toronto, Microsoft, Google, and IBM) ignited a renaissance of applications of deep feedforward neural networks to speech recognition.
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Visual area V2, or secondary visual cortex, also called prestriate cortex,Gazzaniga, Ivry & Mangun: Cognitive neuroscience, 2002 is the second major area in the visual cortex, and the first region within the visual association area. It receives strong feedforward connections from V1 (direct and via the pulvinar) and sends strong connections to V3, V4, and V5. It also sends strong feedback connections to V1. In terms of anatomy, V2 is split into four quadrants, a dorsal and ventral representation in the left and the right hemispheres.
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Researchers argue that the DBM has the ability to model features of cortical learning, perception, and the visual cortex (the locus of visual hallucinations). Compelling evidence details the role homeostatic operations in the cortex play in regards to stabilizing neuronal activity. By using the DBM, researchers show that when sensory input is absent, neuron excitability is influenced, thus potentially triggering complex hallucinations. Acetylcholine Pathway A short-term change in the levels of feedforward and feedback flows of information may intensely affect the presence of hallucinations.
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The classic universal approximation theorem concerns the capacity of feedforward neural networks with a single hidden layer of finite size to approximate continuous functions. In 1989, the first proof was published by George Cybenko for sigmoid activation functions and was generalised to feed-forward multi-layer architectures in 1991 by Kurt Hornik. Recent work also showed that universal approximation also holds for non- bounded activation functions such as the rectified linear unit. The universal approximation theorem for deep neural networks concerns the capacity of networks with bounded width but the depth is allowed to grow.
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The Directions Into Velocities of Articulators (DIVA) model, a feedforward control approach which takes the neural computations underlying speech production into consideration, was developed by Frank H. Guenther at Boston University. The ArtiSynth project,Artisynth headed by Sidney Fels at the University of British Columbia, is a 3D biomechanical modeling toolkit for the human vocal tract and upper airway. Biomechanical modeling of articulators such as the tongue has been pioneered by a number of scientists, including Reiner Wilhelms-Tricarico , Yohan Payan and Jean-Michel Gerard , Jianwu Dang and Kiyoshi Honda .
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Neural networks are different types of simplified mathematical models of biological neural networks like those in human brains. In feedforward neural networks (NNs) the information moves forward in only one direction, from the input layer that receives information from the environment, through the hidden layers to the output layer that supplies the information to the environment. Unlike NNs, recurrent neural networks (RNNs) can use their internal memory to process arbitrary sequences of inputs. If data mining is based on neural networks, overfitting reduces the network's capability to correctly process future data.
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Like rectified linear units (ReLUs), leaky ReLUs (LReLUs), and parametrized ReLUs (PReLUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However, ELUs have improved learning characteristics compared to ReLUs, due to negative values which push mean unit activations closer to zero. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. Sepp Hochreiter introduced self-normalizing neural networks (SNNs) which allow for feedforward networks abstract representations of the input on different levels.
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In fact, feedforward exists as images in the brain, and VSM is just one of many ways to create these simulations. The videos are very short – the best are 1 or 2 minutes long, and achieve changes in behavior very rapidly. Under the right conditions, a very few viewings of these videos can produce skill acquisitions or changes in performance that typically take months and have been resistant to change by other methods. The boy with autism and the girl with selective mutism, mentioned above, are good examples.
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At high frequencies, VY is coupled to the output node directly via collector-base capacitances of the core transistors. Differential VY drive does not eliminate the problem completely due to different capacitances of pnp and npn transistors. Residual VY feedthrough can be nulled by feedforward injection of inverted VY into the output node via a small-value capacitor, restoring capacitive symmetry of the core. Class A cores, in general, are more prone to control voltage feedthrough owing to thermal gradients in the core (in class AB same gradients manifest themselves as distortion).
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The Kohonen net is a computationally convenient abstraction building on biological models of neural systems from the 1970s and morphogenesis models dating back to Alan Turing in the 1950s. While it is typical to consider this type of network structure as related to feedforward networks where the nodes are visualized as being attached, this type of architecture is fundamentally different in arrangement and motivation. Useful extensions include using toroidal grids where opposite edges are connected and using large numbers of nodes. It is also common to use the U-Matrix.
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An electromechanical timer, normally used for open-loop control based purely on a timing sequence, with no feedback from the process. Fundamentally, there are two types of control loop: open-loop (feedforward) control, and closed loop (feedback) control. In open-loop control, the control action from the controller is independent of the "process output" (or "controlled process variable"). A good example of this is a central heating boiler controlled only by a timer, so that heat is applied for a constant time, regardless of the temperature of the building.
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He developed predictive models and implemented these in an integrated design strategy, taking into account all phenomena relevant to the robot's performance. His medical robotic systems perform close to the limits of what is physically possible, allowing highly precise surgical procedures. The design methodology of motion feedforward control was further explored for cooperative driving applications, resulting in a highly cited 2010 IEEE Trans. Vehicular Technology paper. Maarten Steinbuch has supervised over 500 master students, 60 PhD students and published more than 300 peer-reviewed conference contributions and articles in peer-reviewed journals.
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According to French (1991), catastrophic interference arises in feedforward backpropagation networks due to the interaction of node activations, or activation overlap, that occurs in distributed representations at the hidden layer. Neural networks that employ very localized representations do not show catastrophic interference because of the lack of overlap at the hidden layer. French therefore suggested that reducing the value of activation overlap at the hidden layer would reduce catastrophic interference in distributed networks. Specifically he proposed that this could be done through changing the distributed representations at the hidden layer to 'semi-distributed' representations.
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In mathematics, nonlinear modelling is empirical or semi-empirical modelling which takes at least some nonlinearities into account. Nonlinear modelling in practice therefore means modelling of phenomena in which independent variables affecting the system can show complex and synergetic nonlinear effects. Contrary to traditional modelling methods, such as linear regression and basic statistical methods, nonlinear modelling can be utilized efficiently in a vast number of situations where traditional modelling is impractical or impossible. The newer nonlinear modelling approaches include non-parametric methods, such as feedforward neural networks, kernel regression, multivariate splines, etc.
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Stafford Beer's (1979) viable system model is a well-known diagnostic model that comes out of his management cybernetics paradigm. Related to this is the idea of first order and second order cybernetics. Cybernetics is concerned with feedforward and feedback processes, and first order cybernetics is concerned with this relationship between the system and its environment. Second order cybernetics is concerned with the relationship between the system and its internal meta- system (that some refer to as "the observer" of the system). Von FoersterVon Foerster, H (1975). The Cybernetics of Cybernetics, Biological Computer Laboratory, Champaign/Urbana, republished (1995), Future Systems Inc.
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Rapid-chase theory assumes that primes and targets elicit feedforward cascades of neuronal activation traversing the visuomotor system in strict sequence, without mixture or overlap of prime and target signals. Therefore, the initial motor response to the prime must be independent of all stimulus aspects of the actual target. The rapid-chase theory of response priming was proposed in 2006 by Thomas Schmidt, Silja Niehaus, and Annabel Nagel. It ties the direct parameter specification model to findings that newly occurring visual stimuli elicit a wave of neuronal activation in the visuomotor system, which spreads rapidly from visual to motor areas of the cortex.
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The Time Delay Neural Network, like other neural networks, operates with multiple interconnected layers of perceptrons, and is implemented as a feedforward neural network. All neurons (at each layer) of a TDNN receive inputs from the outputs of neurons at the layer below but with two differences: # Unlike regular Multi-Layer perceptrons, all units in a TDNN, at each layer, obtain inputs from a contextual window of outputs from the layer below. For time varying signals (e.g. speech), each unit has connections to the output from units below but also to the time-delayed (past) outputs from these same units.
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For a feedforward neural network, the depth of the CAPs is that of the network and is the number of hidden layers plus one (as the output layer is also parameterized). For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited. No universally agreed upon threshold of depth divides shallow learning from deep learning, but most researchers agree that deep learning involves CAP depth higher than 2. CAP of depth 2 has been shown to be a universal approximator in the sense that it can emulate any function.
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Rajan then worked with David Tank to show that sequential activation of neurons, a common feature in working memory and decision making, can be demonstrated when starting from network models with random connectivity. The process, termed “Partial In-Network Training”, is used as both model and to match real neural data from the posterior parietal cortex during behavior. Rather than feedforward connections, the neural sequences in their model propagate through the network via recurrent synaptic interactions as well as being guided by external inputs. Their modelling highlighted the potential that learning can derive from highly unstructured network architectures.
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Music is often seen in video games and can be a crucial element for influencing the mood of different situations and story points. Machine learning has seen use in the experimental field of music generation; it is uniquely suited to processing raw unstructured data and forming high level representations that could be applied to the diverse field of music. Most attempted methods have involved the use of ANN in some form. Methods include the use of basic feedforward neural networks, autoencoders, restricted boltzmann machines, recurrent neural networks, convolutional neural networks, generative adversarial networks (GANs), and compound architectures that use multiple methods.
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Simple recurrent networks have three layers, with the addition of a set of "context units" in the input layer. These units connect from the hidden layer or the output layer with a fixed weight of one.Neural Networks as Cybernetic Systems 2nd and revised edition, Holk Cruse At each time step, the input is propagated in a standard feedforward fashion, and then a backpropagation-like learning rule is applied (not performing gradient descent). The fixed back connections leave a copy of the previous values of the hidden units in the context units (since they propagate over the connections before the learning rule is applied).
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A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. The term “recurrent neural network” is used indiscriminately to refer to two broad classes of networks with a similar general structure, where one is finite impulse and the other is infinite impulse.
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Practical attempts to building GOFAI soon run into asymptotic increases in algorithmic complexity, so-called 'complexity explosions'. They arise from the control paradigm assumed by GOFAI practitioners, which views governance (= feedforward command + feedback control) as modeling. In other words, for a GOFAI system to 'reason' about the world, it must first build an internal symbolic model of that world upon which it can apply sequences of symbolic manipulations in accord with the principles of Turing and Von Neumann abstract machines. Even small changes in the world must be updated by the GOFAI program, in case they might be critical to its logical output.
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Recent research suggested the existence of an additional feedforward motif linking TSH release to deiodinase activity in humans. The existence of this TSH-T3 shunt could explain why deiodinase activity is higher in hypothyroid patients and why a minor fraction of affected individuals may benefit from substitution therapy with T3. Convergence of multiple afferent signals in the control of TSH release including but not limited to T3, cytokines and TSH receptor antibodies may be the reason for the observation that the relation between free T4 concentration and TSH levels deviates from a pure loglinear relation that has previously been proposed.
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The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) technology, enabled the development of practical artificial neural networks in the 1980s. Computational devices were created in CMOS, for both biophysical simulation and neuromorphic computing. Nanodevices for very large scale principal components analyses and convolution may create a new class of neural computing because they are fundamentally analog rather than digital (even though the first implementations may use digital devices). Ciresan and colleagues (2010) in Schmidhuber's group showed that despite the vanishing gradient problem, GPUs make backpropagation feasible for many-layered feedforward neural networks.
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The process starts when the transcription factor Testis determining factor (encoded by the sex-determining region SRY of the Y chromosome) activates SOX-9 activity by binding to an enhancer sequence upstream of the gene. Next, Sox9 activates FGF9 and forms feedforward loops with FGF9 and PGD2. These loops are important for producing SOX-9; without these loops, SOX-9 would run out and the development of a female would almost certainly ensue. Activation of FGF9 by SOX-9 starts vital processes in male development, such as the creation of testis cords and the multiplication of Sertoli cells.
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Some government research programs focused on intelligence applications of speech recognition, e.g. DARPA's EARS's program and IARPA's Babel program. In the early 2000s, speech recognition was still dominated by traditional approaches such as Hidden Markov Models combined with feedforward artificial neural networks.Herve Bourlard and Nelson Morgan, Connectionist Speech Recognition: A Hybrid Approach, The Kluwer International Series in Engineering and Computer Science; v. 247, Boston: Kluwer Academic Publishers, 1994. Today, however, many aspects of speech recognition have been taken over by a deep learning method called Long short-term memory (LSTM), a recurrent neural network published by Sepp Hochreiter & Jürgen Schmidhuber in 1997.
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The connections and response properties of cells in DM/ V6 suggest that this area is a key node in a subset of the 'dorsal stream', referred to by some as the 'dorsomedial pathway'. This pathway is likely to be important for the control of skeletomotor activity, including postural reactions and reaching movements towards objects The main 'feedforward' connection of DM is to the cortex immediately rostral to it, in the interface between the occipital and parietal lobes (V6A). This region has, in turn, relatively direct connections with the regions of the frontal lobe that control arm movements, including the premotor cortex.
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It is argued that the entire ventral visual-to-hippocampal stream is important for visual memory. This theory, unlike the dominant one, predicts that object-recognition memory (ORM) alterations could result from the manipulation in V2, an area that is highly interconnected within the ventral stream of visual cortices. In the monkey brain, this area receives strong feedforward connections from the primary visual cortex (V1) and sends strong projections to other secondary visual cortices (V3, V4, and V5). Most of the neurons of this area in primates are tuned to simple visual characteristics such as orientation, spatial frequency, size, color, and shape.
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Visual area V4 is one of the visual areas in the extrastriate visual cortex. In macaques, it is located anterior to V2 and posterior to posterior inferotemporal area (PIT). It comprises at least four regions (left and right V4d, left and right V4v), and some groups report that it contains rostral and caudal subdivisions as well. It is unknown whether the human V4 is as expansive as that of the macaque homologue which is a subject of debate. V4 is the third cortical area in the ventral stream, receiving strong feedforward input from V2 and sending strong connections to the PIT.
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The tuning of the synaptic projections between speech sound map and auditory target region map is accomplished by assigning one neuron of the speech sound map to the phonemic representation of that speech item and by associating it with the auditory representation of that speech item, which is activated at the auditory target region map. Auditory regions (i.e. a specification of the auditory variability of a speech unit) occur, because one specific speech unit (i.e. one specific phonemic representation) can be realized by several (slightly) different acoustic (auditory) realizations (for the difference between speech item and speech unit see above: feedforward control) .
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The tuning of the synaptic projections between speech sound map and motor map (i.e. tuning of forward motor commands) is accomplished with the aid of feedback commands, since the projections between sensory error maps and motor map were already tuned during babbling training (see above). Thus the DIVA model tries to "imitate" an auditory speech item by attempting to find a proper feedforward motor command. Subsequently, the model compares the resulting sensory output (current sensory state following the articulation of that attempt) with the already learned auditory target region (intended sensory state) for that speech item.
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His interdisciplinary research on the problem of intelligence, between brains and computers, started at the Max Planck Institute in Tübingen, Germany in collaborations with Werner E. Reichardt, David C. Marr and Francis H.C. Crick, among others. He has made contributions to learning theory, to the computational theory of vision, to the understanding of the fly's visual system, and to the biophysics of computation. His recent work is focused on computational neuroscience in close collaboration with several physiology labs, trying to answer the questions of how our visual system learns to see and recognize scenes and objects.A feedforward architecture accounts for rapid categorization.
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Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need not be tuned. These hidden nodes can be randomly assigned and never updated (i.e. they are random projection but with nonlinear transforms), or can be inherited from their ancestors without being changed. In most cases, the output weights of hidden nodes are usually learned in a single step, which essentially amounts to learning a linear model.
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The generalized Hebbian algorithm (GHA), also known in the literature as Sanger's rule, is a linear feedforward neural network model for unsupervised learning with applications primarily in principal components analysis. First defined in 1989, it is similar to Oja's rule in its formulation and stability, except it can be applied to networks with multiple outputs. The name originates because of the similarity between the algorithm and a hypothesis made by Donald Hebb about the way in which synaptic strengths in the brain are modified in response to experience, i.e., that changes are proportional to the correlation between the firing of pre- and post-synaptic neurons.
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For example, a boy with autism role-plays squeezing a ball (stress management technique) instead of having a tantrum when his work is found imperfect by the teacher – or a selectively mute child is seen on video talking at school, by editing in footage of her talking at home (location disguised by use of a classroom backdrop). By selectively editing a video, a clip was made that demonstrated the desired behavior and allowed the children to learn from their future successes. By reference to its historical context of VSM, it became recognized that feedforward comprised component behaviors already in the repertoire, and that it could exist in forms other than videos.
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In most cases, ELM is used as a single hidden layer feedforward network (SLFN) including but not limited to sigmoid networks, RBF networks, threshold networks, fuzzy inference networks, complex neural networks, wavelet networks, Fourier transform, Laplacian transform, etc. Due to its different learning algorithm implementations for regression, classification, sparse coding, compression, feature learning and clustering, multi ELMs have been used to form multi hidden layer networks, deep learning or hierarchical networks. A hidden node in ELM is a computational element, which need not be considered as classical neuron. A hidden node in ELM can be classical artificial neurons, basis functions, or a subnetwork formed by some hidden nodes.
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Previous research shows that better LMX results in more resources being available to subordinates and restricted information. Employees in a mobile phone company with better LMX, characterized with a high degree of mutual trust, were more willing to share their knowledge Li, R.Y.M., Tang, B., Chau, K.W. (2019) "Sustainable Construction Safety Knowledge Sharing: A Partial Least Square-Structural Equation Modeling and A Feedforward Neural Network Approach" "Sustainability" 2019, 11(20), 5831, doi.org/10.3390/su11205831. The latest version (2016) of leader–member exchange theory of leadership development explains the growth of vertical dyadic workplace influence and team performance in terms of selection and self-selection of informal apprenticeships in leadership.Graen, G. B. & Canedo, J. (2016).
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The main technological change for the higher-capacity formats was the addition of tracking information on the disk surface to allow the read/write heads to be positioned more accurately. Normal disks have no such information, so the drives use feedforward (blind) positioning by a stepper motor in order to position their heads over the desired track. For good interoperability of disks among drives, this requires precise alignment of the drive heads to a reference standard, somewhat similar to the alignment required to get the best performance out of an audio tape deck. The newer systems generally use position information on the surfaces of the disk to find the tracks, allowing the track width to be greatly reduced.
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Perceptual control theory (PCT) is a psychological theory of animal and human behavior originated by William T. Powers. In contrast with other theories of psychology and behavior, which assume that behavior is a function of perception – that perceptual inputs determine or cause behavior – PCT postulates that an organism's behavior is a means of controlling its perceptions. In contrast with engineering control theory, the reference variable for each negative feedback control loop in a control hierarchy is set from within the system (the organism), rather than by an external agent changing the setpoint of the controller.Engineering control theory also makes use of feedforward, predictive control, and other functions that are not required to model the behavior of living organisms.
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Then the model updates the current feedforward motor command by the current feedback motor command generated from the auditory error map of the auditory feedback system. This process may be repeated several times (several attempts). The DIVA model is capable of producing the speech item with a decreasing auditory difference between current and intended auditory state from attempt to attempt. During imitation the DIVA model is also capable of tuning the synaptic projections from speech sound map to somatosensory target region map, since each new imitation attempt produces a new articulation of the speech item and thus produces a somatosensory state pattern which is associated with the phonemic representation of that speech item.
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Variants of the back-propagation algorithm as well as unsupervised methods by Geoff Hinton and colleagues at the University of Toronto can be used to train deep, highly nonlinear neural architectures, similar to the 1980 Neocognitron by Kunihiko Fukushima, and the "standard architecture of vision", inspired by the simple and complex cells identified by David H. Hubel and Torsten Wiesel in the primary visual cortex. Radial basis function and wavelet networks have also been introduced. These can be shown to offer best approximation properties and have been applied in nonlinear system identification and classification applications. Deep learning feedforward networks alternate convolutional layers and max-pooling layers, topped by several pure classification layers.
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Ans and Rousset (1997) also proposed a two-network artificial neural architecture with memory self-refreshing that overcomes catastrophic interference when sequential learning tasks are carried out in distributed networks trained by backpropagation. The principle is to interleave, at the time when new external patterns are learned, those to-be-learned new external patterns with internally generated pseudopatterns, or 'pseudo-memories', that reflect the previously learned information. What mainly distinguishes this model from those that use classical pseudorehearsal in feedforward multilayer networks is a reverberating process that is used for generating pseudopatterns. After a number of activity re-injections from a single random seed, this process tends to go up to nonlinear network attractors.
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Working memory is impaired by acute and chronic psychological stress. This phenomenon was first discovered in animal studies by Arnsten and colleagues, who have shown that stress-induced catecholamine release in PFC rapidly decreases PFC neuronal firing and impairs working memory performance through feedforward, intracellular signaling pathways. Exposure to chronic stress leads to more profound working memory deficits and additional architectural changes in PFC, including dendritic atrophy and spine loss, which can be prevented by inhibition of protein kinase C signaling. fMRI research has extended this research to humans, and confirms that reduced working memory caused by acute stress links to reduced activation of the PFC, and stress increased levels of catecholamines.
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Later it was combined with connectionist temporal classification (CTC) in stacks of LSTM RNNs.Santiago Fernandez, Alex Graves, and Jürgen Schmidhuber (2007). An application of recurrent neural networks to discriminative keyword spotting. Proceedings of ICANN (2), pp. 220–229. In 2015, Google's speech recognition reportedly experienced a dramatic performance jump of 49% through CTC-trained LSTM, which they made available through Google Voice Search. In 2006, publications by Geoff Hinton, Ruslan Salakhutdinov, Osindero and Teh showed how a many-layered feedforward neural network could be effectively pre- trained one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then fine-tuning it using supervised backpropagation.G. E. Hinton., "Learning multiple layers of representation," Trends in Cognitive Sciences, 11, pp.
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This feedforward mode of operation means that the cerebellum, in contrast to the cerebral cortex, cannot generate self-sustaining patterns of neural activity. Signals enter the circuit, are processed by each stage in sequential order, and then leave. As Eccles, Ito, and Szentágothai wrote, "This elimination in the design of all possibility of reverberatory chains of neuronal excitation is undoubtedly a great advantage in the performance of the cerebellum as a computer, because what the rest of the nervous system requires from the cerebellum is presumably not some output expressing the operation of complex reverberatory circuits in the cerebellum but rather a quick and clear response to the input of any particular set of information."The Cerebellum as a Neuronal Machine, p.
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In addition, a three-way manual override switch enables the driver to select tarmac, gravel or snow modes to suit his preferences or driving conditions. The ACD also frees the differential on operation of the hand brake, allowing the driver to make more effective use of side brake turns in rallies and gymkhanas. On the Evolution VII, control of the ACD and AYC systems is integrated for the very first time (integrated management of these systems is the core of Mitsubishi's AWC philosophy). In the integrated system, ACD feedback and feedforward information is transmitted to the AYC control system using parameters in such a way that the larger the ACD differential limiting force is, the larger the yaw moment generated by the AYC system.
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The equivalence between infinitely wide Bayesian neural networks and NNGPs has been shown to hold for: single hidden layer and deep fully connected networks as the number of units per layer is taken to infinity; convolutional neural networks as the number of channels is taken to infinity; transformer networks as the number of attention heads is taken to infinity; recurrent networks as the number of units is taken to infinity. In fact, this NNGP correspondence holds for almost any architecture: Generally, if an architecture can be expressed solely via matrix multiplication and coordinatewise nonlinearities (i.e. a tensor program), then it has an infinite-width GP . This in particular includes all feedforward or recurrent neural networks composed of multilayer perceptron, recurrent neural networks (e.g.
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A feed forward, sometimes written feedforward, is an element or pathway within a control system that passes a controlling signal from a source in its external environment to a load elsewhere in its external environment. This is often a command signal from an external operator. A control system which has only feed-forward behavior responds to its control signal in a pre-defined way without responding to how the load reacts; it is in contrast with a system that also has feedback, which adjusts the input to take account of how it affects the load, and how the load itself may vary unpredictably; the load is considered to belong to the external environment of the system. In a feed- forward system, the control variable adjustment is not error-based.
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The Schwann cell promoter is present in the downstream region of the human dystrophin gene that gives shortened transcript that are again synthesized in a tissue-specific manner. During the development of the PNS, the regulatory mechanisms of myelination are controlled by feedforward interaction of specific genes, influencing transcriptional cascades and shaping the morphology of the myelinated nerve fibers. Schwann cells are involved in many important aspects of peripheral nerve biology—the conduction of nervous impulses along axons, nerve development and regeneration, trophic support for neurons, production of the nerve extracellular matrix, modulation of neuromuscular synaptic activity, and presentation of antigens to T-lymphocytes. Charcot–Marie–Tooth disease, Guillain–Barré syndrome (acute inflammatory demyelinating polyradiculopathy type), schwannomatosis, chronic inflammatory demyelinating polyneuropathy, and leprosy are all neuropathies involving Schwann cells.
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The act of seeing starts when the cornea and then the lens of the eye focuses light from its surroundings onto a light-sensitive membrane in the back of the eye, called the retina. The retina is actually part of the brain that is isolated to serve as a transducer for the conversion of light into neuronal signals. Based on feedback from the visual system, the lens of the eye adjusts its thickness to focus light on the photoreceptive cells of the retina, also known as the rods and cones, which detect the photons of light and respond by producing neural impulses. These signals are processed via complex feedforward and feedback processes by different parts of the brain, from the retina upstream to central ganglia in the brain.
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The part of the Y-chromosome which is responsible for maleness is the sex-determining region of the Y-chromosome, the SRY. The SRY activates Sox9, which forms feedforward loops with FGF9 and PGD2 in the gonads, allowing the levels of these genes to stay high enough in order to cause male development; for example, Fgf9 is responsible for development of the spermatic cords and the multiplication of Sertoli cells, both of which are crucial to male sexual development. The ZW sex-determination system, where males have a ZZ (as opposed to ZW) sex chromosome may be found in birds and some insects (mostly butterflies and moths) and other organisms. Members of the insect order Hymenoptera, such as ants and bees, are often determined by haplodiploidy, where most males are haploid and females and some sterile males are diploid.
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Since 2009, the recurrent neural networks and deep feedforward neural networks developed in the research group of Jürgen Schmidhuber at the Swiss AI Lab IDSIA have won several international handwriting competitions.2012 Kurzweil AI Interview with Jürgen Schmidhuber on the eight competitions won by his Deep Learning team 2009-2012 In particular, the bi-directional and multi- dimensional Long short-term memory (LSTM)Graves, Alex; and Schmidhuber, Jürgen; Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, in Bengio, Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris K. I.; and Culotta, Aron (eds.), Advances in Neural Information Processing Systems 22 (NIPS'22), December 7th–10th, 2009, Vancouver, BC, Neural Information Processing Systems (NIPS) Foundation, 2009, pp. 545–552A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, J. Schmidhuber. A Novel Connectionist System for Improved Unconstrained Handwriting Recognition.
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In Qianli Liao, Joel Z Leibo, Youssef Mroueh, Tomaso Poggio (2014) Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines? CBMM Memo No. 003Qianli Liao, Joel Z Leibo, and Tomaso Poggio (2014) Learning invariant representations and applications to face verification NIPS 2014 authors applied M-theory to unconstrained face recognition in natural photographs. Unlike the DAR (detection, alignment, and recognition) method, which handles clutter by detecting objects and cropping closely around them so that very little background remains, this approach accomplishes detection and alignment implicitly by storing transformations of training images (templates) rather than explicitly detecting and aligning or cropping faces at test time. This system is built according to the principles of a recent theory of invariance in hierarchical networks and can evade the clutter problem generally problematic for feedforward systems.
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Fiete was then interested in developing a robust system with which to determine neural circuit mechanisms underlying brain function that do not merely rely on observing neural activity. Using the grid cell system, which Fiete had extensively probed and serves as a good system for testing computational models, Fiete showed that the "distribution of relative phase shifts" model has the potential to reveal highly detailed cortical circuit mechanisms from sparse neural recordings. Through the use of perturbative experiments, they find that their method is able to discriminate between feedforward and recurrent neural networks to uncover which model most accurately described neural computations. In 2019, once Fiete had arrived at MIT, she published a paper using topological modeling to transform the neural activity of large populations of neurons into a data cloud representing the shape of a ring.
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From 2001-2010, ELM research mainly focused on the unified learning framework for "generalized" single-hidden layer feedforward neural networks (SLFNs), including but not limited to sigmoid networks, RBF networks, threshold networks, trigonometric networks, fuzzy inference systems, Fourier series, Laplacian transform, wavelet networks, etc. One significant achievement made in those years is to successfully prove the universal approximation and classification capabilities of ELM in theory. From 2010 to 2015, ELM research extended to the unified learning framework for kernel learning, SVM and a few typical feature learning methods such as Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF). It is shown that SVM actually provides suboptimal solutions compared to ELM, and ELM can provide the whitebox kernel mapping, which is implemented by ELM random feature mapping, instead of the blackbox kernel used in SVM.
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His laboratory has demonstrated the molecular mechanisms that pattern the fly color-sensing photoreceptor neurons and showed how stochastic decisions,Losick R. & Desplan C. Stochastic choices and cell fate, Science 320, 65-68 (2008)Johnston R.J.Jr. & Desplan C. Interchromosomal communication coordinates intrinsically stochastic expression between alleles. Science 343:661-5 (2014) a transcription factor network,Johnston R. Jr. Otake Y., Sood P., Vogt N., Behnia R., Vasiliauskas.D, McDonald E., Xie B., Koenig ., Wolf R., Cook T., Gebelein B., Kussell E., Nagakoshi H. & Desplan C. Interlocked feedforward loops control specific Rhodopsin expression in the Drosophila eye. Cell 145, 956-968 (2011) and a tumor suppressor pathwayMikeladze-Dvali T., Wernet M., Pistillo D. , Mazzoni E. O., Teleman A., Chen Y., Cohen S. & Desplan C. The growth regulators Warts/lats and Melted interact in a bistable loop to specify opposite fates in R8 photoreceptors. Cell, 122, 775-787 (2005).
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For humans, those tendencies lead to an error in development: we create collective units that are based on the oppression of some individuals and on the inflated egos of others. This is for Koestler an error of transcendence that is reflected in a poor integration of our reptilian brain and cognitive brain. A superposition of forces manifests, at each bodily holon, as the outcome of an entire hierarchy of forces—ontogenetic, habitual, linguistic prescriptive, and social science—operating in a continuum of independent feedback and feedforward streams of a body extended to its larger environment. The streams are fed by the life signals of each and every group member, and this fully participative medley is the spirit of life one senses as a ghost; but this spirit is just a simplified output of a complex knowledge set; it is emergent from the complexity of the group's rules and strategies.
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Weights were encoded in potentiometers, and weight updates during learning were performed by electric motors. In a 1958 press conference organized by the US Navy, Rosenblatt made statements about the perceptron that caused a heated controversy among the fledgling AI community; based on Rosenblatt's statements, The New York Times reported the perceptron to be "the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence." Although the perceptron initially seemed promising, it was quickly proved that perceptrons could not be trained to recognise many classes of patterns. This caused the field of neural network research to stagnate for many years, before it was recognised that a feedforward neural network with two or more layers (also called a multilayer perceptron) had greater processing power than perceptrons with one layer (also called a single layer perceptron).
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Between 2009 and 2012, recurrent neural networks and deep feedforward neural networks developed in Schmidhuber's research group won eight international competitions in pattern recognition and machine learning.2012 Kurzweil AI Interview with Jürgen Schmidhuber on the eight competitions won by his Deep Learning team 2009–2012 For example, the bi-directional and multi-dimensional long short-term memory (LSTM)Graves, Alex; and Schmidhuber, Jürgen; Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, in Bengio, Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris K. I.; and Culotta, Aron (eds.), Advances in Neural Information Processing Systems 22 (NIPS'22), 7–10 December 2009, Vancouver, BC, Neural Information Processing Systems (NIPS) Foundation, 2009, pp. 545–552. of Graves et al. won three competitions in connected handwriting recognition at the 2009 International Conference on Document Analysis and Recognition (ICDAR), without any prior knowledge about the three languages to be learned.
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More recent efforts show promise for creating nanodevices for very large scale principal components analyses and convolution. If successful, these efforts could usher in a new era of neural computing that is a step beyond digital computing, because it depends on learning rather than programming and because it is fundamentally analog rather than digital even though the first instantiations may in fact be with CMOS digital devices. Between 2009 and 2012, the recurrent neural networks and deep feedforward neural networks developed in the research group of Jürgen Schmidhuber at the Swiss AI Lab IDSIA have won eight international competitions in pattern recognition and machine learning. For example, multi- dimensional long short term memory (LSTM) won three competitions in connected handwriting recognition at the 2009 International Conference on Document Analysis and Recognition (ICDAR), without any prior knowledge about the three different languages to be learned.
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In the mathematical theory of artificial neural networks, universal approximation theorems are resultsBalázs Csanád Csáji (2001) Approximation with Artificial Neural Networks; Faculty of Sciences; Eötvös Loránd University, Hungary that establish the density of an algorithmically generated class of functions within a given function space of interest. Typically, these results concern the approximation capabilities of the feedforward architecture on the space of continuous functions between two Euclidean spaces, and the approximation is with respect to the compact convergence topology. However, there are also a variety of results between non-Euclidean spaces and other commonly used architectures and, more generally, algorithmically generated sets of functions, such as the convolutional neural network (CNN) architecture, Zhou, Ding-Xuan (2020) Universality of deep convolutional neural networks; Applied and computational harmonic analysis 48.2 (2020): 787-794. A. Heinecke, J. Ho and W. Hwang (2020); Refinement and Universal Approximation via Sparsely Connected ReLU Convolution Nets; IEEE Signal Processing Letters, vol.
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