Papers focusing on how biological systems learn. Topics include laminar computing, cognitive emotional interaction, and sequence learning, etc.
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How does the brain learn to recognize an object from multiple viewpoints while scanning a scene with eye movements? How does the brain avoid the problem of erroneously classifying parts of different objects together? How are ... |
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Neural networks are applied to a theoretical subject in developmental psychology: modeling developmental transitions. Two issues that are involved will be discussed: discontinuities and acquiring qualitatively new knowledge. ... |
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This paper makes some neurophysiological and biochemical predictionsconcerning transmitter production and release which are suggested bypsychological postulates. A main theme is the joint comrol of presynapticexcitatory ... |
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Many of our sensory and motor organs have linearly ordered components, for example the fingers on a hand, the tonotopic organization of the auditory system, the successivjeo ints on arms and legs,t he spine,e tc. This paper ... |
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A learning theory in continuous time is derived herein from simple psychologicalpostulates. The theory has an anatomical and neurophysiological interpretation interms of nerve cell bodies, axons, synaptic knobs, membrane ... |
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Neural networks are introduced which can be taught by classical or instrumental conditioning to fire in response to arbitrary learned classes of patterns. The filters of output cells are biased by presetting cells whose ... |
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A real-time neural network model, called affective balance theory, is developed to explain many properties of decision making under risk that heretofore have been analyzed using formal algebraic models, notably prospect ... |
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Reeves and Sperling (1986) have developed an experimental paradigm and a model to explain how attention switching influences the, storage oftemporal-order information in short-term memory (STM), or working memory. The ... |
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Data and models about recognition and recall of words and non words are unified using a real-time network processing theory. Lexical decision and word frequency effect data are analyzed in terms of theoretical concepts that ... |
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A real-time neural network model is developed to explain data about the acquisition and extinction of conditioned excitors and inhibitors. Systematic computer simulations are described of a READ circuit, which joins together ... |
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Opponent processes have long been known to be a basic building block of neural circuits. This article describes properties of opponent processes in which phasic cues and tonic arousal are gated by slowly accumulating ... |
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Summary. A computational theory of how an observer parses a speech stream into context-sensitive language representations is described. It is shown how temporal lists of events can be chunked into unitized repre~ntations, ... |
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Possible dependencies of serial learning data on physiological parameters such as spiking thresholds, arousal level, and decay rate of potentials are considered in a rigorous learning model. Influence of these parameters on ... |
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Simple psychological postulates are presented which are used to derivepossible anatomical and physiological substrates of operant conditioning.These substrates are compatible with much psychological data aboutoperants. A ... |
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A theory is presented of how global visual interactions between depth, length, lightness, and form percepts can occur. The theory suggests how quantized activity patterns which reflect these visual properties can coherently ... |
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Some recent formal models of Pavlovian and instrumental conditioning contain internal paradoxes that restrict their predictive power. These paradoxes can be traced to an inadequate formulation of how mechanisms of ... |
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Samuel, van Santen, and Johnston (1982,1983) reported a word length effect in a word superiority paradigm. A word length effect was predicted in Grossberg (1978a). This article describes the main concepts about the ... |
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Some possible neural mechanisms of pattern discrimination are discussed, leading to neural networks which can discriminate any number of essentially arbitrarily complicated space-time patterns and activate cells which can ... |
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This paper describes mechanisms of intracellular and intercellular adaptation that are due to spatial or temporal factors. The spatial mechanisms support self-regulating pattern formation that is capable of directing ... |
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This article reviews results on a learning theory that can be derived from simple psychological postulates and given a suggestive neurophysiological, anatomical, and biochemical interpretation. The neural networks described ... |
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A model of the nonlinear dynamics of reverberating on-center off-surround networks of nerve cells, or of cell populations, is analysed. The on-center off-surround anatomy allows patterns to be processed across populations ... |
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Competition solves a universal problem about pattern processing by cellular systems. Competition allows cells to automatically retune their sensitivity to avoid noise and saturation effects. All competitive systems induce ... |
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A well-known process for adaptation and gain normalization is compared with the process described by S. Ullman and G. Schechtman (Proc. R. Soc. Lond. B 216, 299-313 (1982)). A neural interpretation of this process in terms ... |
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The following problem, in one form or another, has intrigued philosophers and scientists for hundreds of years: How do arbitrarily many individuals, populations, or states, each obeying unique and personal laws, ever succeed ... |
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Three experiments were conducted to evaluate the P300 component of the human evoked response as an index of bisensory information processing. On different blocks of trials, subjects were presented with auditory stimuli ... |
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Neural networks are derived from psychological postulates about punishment and avoidance. The classical notion that drive reduction is reinforcing is replaced by a precise physiological alternative akin to Miller s ""Go"" ... |
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This paper develops a model wherein STM primacy as well as recency effects can occur. The STM primacy effects can be used to generate correct immediate recall of short lists that have not been coded in LTM. The properties of ... |
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Selective information processing in neural networks is studied through computer simulations of Pavlovian conditioning data. The model reproduces properties of blocking, inverted-U in learning as a function of interstimulus ... |
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Quantitative neural networks are derived from psychological postulates about punishment and avoidance. The classical notion that drive reduction is reinforcing is replaced by a precise physiological altetAative akin to ... |
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Data reporting correlated changes, due to learning, in the amplitudes and chronometry of several eventrelated potentials (ERPs) are compared to neural explanations and predictions of the adaptive resonance theory. The ERP ... |
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Computer simulations of neural network processes fill an important methodological niche, permitting the investigation of questions not resolvable by physiological, behavioral, or formal approaches alone. Two types of network ... |
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A neural network model that controls behavioral timing is described and simulated. This model, called the Spectral Timing Model, controls a type of timing whereby an animal or robot can learn to wait for an expectedg oal by ... |
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A real-time neural network model. called the vector-integration-to-endpoint( VITE) model is developed and used to simulate quantitatively behavioral and neural data about planned and passive arm movements.I nvariants of arm ... |
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A real time neural network model is described in which reinforcement helps to focus attention upon and organize learning of those environmental events and contingencies that have predicted behavioral success in the past. ... |
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A large body of data is reviewed to support a new theory of motion perception described by S. Grossberg and M. E. Rudd (1989). The Motion Boundary Contour System is used to explain classical and recent data about motion ... |
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A neural network model is described for adaptive control of arm movement trajectories during visually guided reaching. The model clarifies how a child, or infant robot, can learn to reach for objects that it sees. Piaget has ... |
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A neural network model of global motion segmentation by visual cortex is described. Called the motion boundary contour system (BCS), the model clarifies how ambiguous local movements on a complex moving shape are actively ... |
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This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models ... |
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This article analyzes computational properties that clarify why the parallel cortical systems V1----V2, V1----MT, and V1----V2----MT exist for the perceptual processing of static visual forms and moving visual forms. The ... |
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The origin of the tri-phasic burst pattern, observed in the EMGs of opponent muscles during rapid self-terminated movements, has benn controversial. Here we show by computer simulation that the pattern emerges from ... |
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This article describes two neural network modules that form part of an emerging theory of how adaptive control of goal-directed sensory-motor skills is achieved by humans and other animals. The Vector-1ntegration-To-Endpoint ... |
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A neural model is described of how adaptively timed reinforcement learning occurs. The adaptive timing circuit is suggested to exist in the hippocampus, and to involve convergence of dentate granule cells on CA3 pyramidal ... |
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This paper describes a self-organizing neural model for eye-hand coordination. Called the DIRECT model, it embodies a solution of the classical motor equivalence problem. Motor equivalence computations allow humans and other ... |
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How do humans and other animals accomplish coordinated movements? How are novel combinations of limb joints rapidly assembled into new behavioral units that move together in in-phase or anti-phase movement patterns during ... |
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The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, ... |
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A neural model is developed of how motion integration and segmentation processes compute global motion percepts. Figure-ground properties, such as occlusion, influence which motion signals determine the percept. For visible ... |
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Behavioral data concerning animal and human gaits and gait transitions are simulated as emergent properties of a central pattern generator (CPG) model. The CPG model is a version of the Ellias-Grossberg oscillator. Its ... |
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What is the neural representation of a speech code as it evolves in time? A neural model simulates data concerning segregation and integration of phonetic percepts. Hearing two phonetically related stops in a VC-CV pair (V = ... |
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To understand how the cerebellum adaptively times the classically conditioned nictitating membrane response (NMR), a model of the metabotropic glutamate receptor (mGluR) second messenger system in cerebellar Purkinje cells ... |
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How does the brain group together different parts of an object into a coherent visual object representation? Different parts of an object may be processed by the brain at different rates and may thus become desynchronized. ... |
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A model of cortico-spinal trajectory generation for voluntary reaching movements is developed to functionally interpret a broad range of behavioral, physiological, and anatomical data. The model simulates how arm movements ... |
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Prenatal development of the primary visual cortex leads to simple cells with spatially distinct and oriented ON and OFF subregions. These simple cells are organized into spatial maps of orientation and ocular dominance that ... |
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A neural network model of opponent cerebellar learning for arm movement control is proposed. The model illustrates how a central pattern generator in cortex and basal ganglia, a neuromuscular force controller in spinal cord, ... |
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A neural model of voluntary movement and proprioception is developed that offers an integrated interpretation of the functional roles of diverse cell types in movement-related areas of primate cortex. The model circuit ... |
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The thresholds of human observers detecting line targets improve significantly when the targets are presented in a spatial context of collinear inducing stimuli. This phenomenon is referred to as spatial facilitation, and ... |
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Smooth pursuit eye movements (SPEMs) are eye rotations that are used to maintain fixation on a moving target. Such rotations complicate the interpretation of the retinal image, because they nullify the retinal motion of the ... |
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Experiments by Markram and Tsodyks (Nature, 382 (1996) 807?810) have suggested that Hebbian pairing in cortical pyramidal neurons potentiates or depresses the transmission of a subsequent pre-synaptic spike train at ... |
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This article outlines a methodology for investigating the coordinate systems by which movement variables are encoded in the firing rates of individual motor cortical neurons. Recent neurophysiological experiments have probed ... |
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This article suggests how brain mechanisms of learning, attention, and volition may give rise to hallucinations during schizophrenia and other mental disorders. The article suggests that normal learning and memory are ... |
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How are our brains functionally organized to achieve adaptive behavior in a changing world? This article presents one alternative to the computer analogy that suggests brains are organized into independent modules. Evidence ... |
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During goal-directed reaching in primates, a sensorimotor transformation generates a dynamical pattern of muscle activation. Within the context of this sensorimotor transformation, a fundamental question concerns the ... |
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An outstanding problem in psychiatry concerns how to link discoveries about the pharmacological, neurophysiological, and neuroanatomical substrates of mental disorders to the abnormal behaviors that they control. A related ... |
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After classically conditioned learning, dopaminergic cells in the substantia nigra pars compacta (SNc) respond immediately to unexpected conditioned stimuli (CS) but omit formerly seen responses to expected unconditioned ... |
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How does the brain learn to balance between reactive and planned behaviors? The basal ganglia (BG) and frontal cortex together allow animals to learn planned behaviors that acquire rewards when prepotent reactive behaviors ... |
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The 3D LAMINART neural model is developed to explain how the visual cortex gives rise to 3D percepts of stratification, transparency, and neon color spreading in response to 2D pictures and 3D scenes. Such percepts are ... |
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The concept of attention has been used in many senses, often without clarifying how or why attention works as it does. Attention, like consciousness, is often described in a disembodied way. The present article summarizes ... |
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What brain mechanisms underlie autism, and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the Imbalanced Spectrally Timed Adaptive Resonance Theory (iSTART) model, that ... |
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The hippocampus participates in multiple functions, including spatial navigation, adaptive timing and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes ... |
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A full understanding of consciousness requires that we identify the brain processes from which conscious experiences emerge. What are these processes, and what is their utility in supporting successful adaptive behaviors? ... |
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It is a neural network truth universally acknowledged, that the signal transmitted to a target node must be equal to the product of the path signal times a weight. Analysis of catastrophic forgetting by distributed codes ... |
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Myriad signals relentlessly bombard our senses. These signals may arrive in disconnected pieces, yet we can integrate them as unified moments of conscious experience. the apparent singularity and coherence of an experience ... |
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The processes by which humans and other primates learn to recognize objects have been the subject of many models. Processes such as learning, categorization, attention, memory search, expectation and novelty detection work ... |
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How the mammalian brain can rapidly but stably learn about a changing world filled with unexpected events is one of the most challenging scientific problems of our time. The brain?s ability to autonomously discover and ... |
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In this chapter, we discuss some recent results in neural networks relevant to adaptive pattern recognition and sensory-motor control problems. IN biologically-oriented neural networks, whose fast dynamics are governed by ... |
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A theory is developed of how recognition categories can be learned in response to a temporal stream of input patterns. Interactions between an attentional subsystem and an orienting subsystem enable the network to ... |
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An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter ... |
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The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to ... |
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A neural network architecture for the learning of recognition categories is derived. Real-time network dynamics are completely characterized through mathematical analysis and computer simulations. The architecture ... |
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A neural theory of the circadian pacemaker within the hypothalamic suprachiasmatic nuclei (SCN) is used to explain parametric data about mammalian operant behavior. The intensity, duration, and patterning of ultradian ... |
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A theory is presented of how recognition categories can be learned in response to a temporal stream of input patterns. Interactions between an attentional subsystem and an orienting subsystem enable the network to ... |
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This article describes a behaviorally, physiologically, and anatomically predictive model of how circadian rhythms are generated by each suprachiasmatic nucleus (SCN) of the mammalian hypothalamus. This gated pacemaker model ... |
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A neural model of the suprachiasmatic nuclei suggests how behavioral activity, rest, and circadian period depend on light intensity in diurnal and nocturnal mammals. these properties are traced to the action of light input ... |
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The lamprey occupies a strategic evolutionary position in terms of the organization of its nervous system (1), and serves as an important cellular model for recovery from spinal cord lesions (2) and for the analysis of motor ... |
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A quantitative model for the transduction dynamics whereby intracellular transmitter in a vertebrate cone mediates between light input and voltage output is analyzed. a basic postulate is that the transmitter acts to ... |
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This article indicates how competition between afferent data and learned feed-back expectancies can stabilize a developing code by buffering committed populations of detectors against continual erosion by new environmental ... |
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A psychophysiological theory of the self-organization and performance of sensory-motor codes, maps, and plans is derived herein. this general topic includes a variety of phenomena in many species, ranging from the ... |
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This article reviews results chosen from the theory of embedding fields. Embedding field theory discusses mechanisms of pattern discrimination and learning in a psychophysiological setting. It is derived from psychological ... |
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How do humans and animals learn to recognize objects and events? Two classical views are that exemplars or prototypes are learned. A hybrid view is that a mixture, called rule-plus-exceptions, is learned. None of these ... |
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This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and ... |
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Real-time neural-network models provide a conceptual framework for formulating questions about the nature of cognition, an architectural framework for mapping cognitive functions to brain regions, a semantic framework for ... |
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Principles derived from an analysis of experimental literatures in vision, speech, cortical development, and reinforcement learning, including attentional blocking and cognitive-emotional interactions, led to the ... |
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A neural network model provides behavioral, physiological, and anatomical predictions of how circadian rhythms are generated by the suprachiasmatic nuclei (SCN) of the mammalian hypothalamus. The 4-dimensional basic gated ... |
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Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose ... |
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A neural model suggests how horizontal and interlaminar connections in visual cortical areas V1 and V2 develop within a laminar cortical architecture and give rise to adult visual percepts. The model suggests how mechanisms ... |
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Both animals and humans represent and compare numerical quantities, but only humans have evolved multi-digit place-value number systems. This article develops a Spatial Number Network, or SpaN, model to explain how these ... |
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One of the most exciting and open research frontiers in neuroscience is that of seeking to understand the functional roles of the layers of cerebral cortex. New experimental techniques for probing the laminar circuitry of ... |
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A key goal of behavioral and cognitive neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how the visual cortex sees. Visual cortex, like many ... |
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This article introduces neural models as a link between neuroscience discoveries of brain mechanisms controlling behavior and psychiatric imbalances leading to behavioral ... |
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A model to demonstrate the effect of Excitatory Postsynaptic Potentials (EPSP) and Inhibitory Postsynaptic Potentials (IPSP) on a neuron. The model is based on synaptic conductance equations from (Kohn and Worgotter 1998) ... |
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Outstar learning law (Grossberg, 1976) governs the dynamics of feedback connection weights in a standard competitive neural network in an unsupervised manner. This learning models how a neuron can learn a top-down template ... |
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Instar learning law (Grossberg, 1976) governs the dynamics of feedforward connection weights in a standard competitive neural network in an unsupervised manner. This learning models how a neuron can become selectively ... |
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This entry contains the software, implemented in the KDE Integrated NeuroSimulation Software (KInNeSS ) that simulates the Synchronous Matching Adaptive Resonance Theory. SMART was first described in Grossberg and Versace ... |
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