Building network learning algorithms from Hebbian synapses

In 1949 Donald Hebb published The Organization of Behavior, in which he introduced several hypotheses about the neural substrate of learning and memory, including the Hebb learning rule, or Hebb synapse. We now have solid physiological evidence, verified in several laboratories, that long-term potentiation (LTP) in some parts of the mammalian hippocampus follows the Hebb rule (Brown, Ganong, Kariss, Keenan, & Kelso, 1989; Kelso, Ganong, & Brown, 1986; Levy, Brassel, & Moore, 1983; McNaughton, Douglas, & Goddard, 1978; McNaughton & Morris, 1987; Wigstrom and Gustafsson, 1985). The Hebb rule and variations on it have also served as the starting point for the study of information storage in simplified "neural network" models (Hopfield & Tank, 1986; Kohonen, 1984; Rumelhart & McClelland, 1986; Sejnowski, 1981). Many types of networks have been studied-networks with random connectivity, networks with layers, networks with feedback between layers, and a wide variety of local patterns of connectivity. Even the simplest network model has complexities that are difficult to analyze. In this chapter we will provide a framework within which the Hebb rule serves as an important link between the implementation level of analysis, the level at which experimental work on neural mechanisms takes place, and the algorithmic level, on which much of the work on learning in network models is being pursued.

[1]  T. Sejnowski Computational models and the development of topographic projections , 1987, Trends in Neurosciences.

[2]  Idan Segev,et al.  Signal enhancement in distal cortical dendrites by means of interactions between active dendritic spines. , 1985, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Y. Frégnac,et al.  Development of neuronal selectivity in primary visual cortex of cat. , 1984, Physiological reviews.

[4]  G. Stent A physiological mechanism for Hebb's postulate of learning. , 1973, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[6]  Y. Frégnac,et al.  A cellular analogue of visual cortical plasticity , 1988, Nature.

[7]  D. Alkon,et al.  Cellular mechanisms of learning, memory, and information storage. , 1985, Annual review of psychology.

[8]  E. Kandel,et al.  Is there a cell-biological alphabet for simple forms of learning? , 1984, Psychological review.

[9]  R Linsker,et al.  From basic network principles to neural architecture: emergence of orientation columns. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[11]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[12]  D. Fayuk,et al.  The Journal of Physiology , 1978, Medical History.

[13]  K Toyama,et al.  Long-term potentiation of synaptic transmission in kitten visual cortex. , 1988, Journal of neurophysiology.

[14]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[15]  A. Klopf A neuronal model of classical conditioning , 1988 .

[16]  B. McNaughton,et al.  Synaptic enhancement in fascia dentata: Cooperativity among coactive afferents , 1978, Brain Research.

[17]  D. GABOR,et al.  Holographic Model of Temporal Recall , 1968, Nature.

[18]  D. Marr A theory of cerebellar cortex , 1969, The Journal of physiology.

[19]  Geoffrey E. Hinton,et al.  OPTIMAL PERCEPTUAL INFERENCE , 1983 .

[20]  L. Cooper,et al.  A physiological basis for a theory of synapse modification. , 1987, Science.

[21]  R. Sutton,et al.  Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element: Response topography, neuronal firing, and interstimulus intervals , 1986, Behavioural Brain Research.

[22]  Teuvo Kohonen,et al.  Correlation Matrix Memories , 1972, IEEE Transactions on Computers.

[23]  M. Ito Cerebellar control of the vestibulo-ocular reflex--around the flocculus hypothesis. , 1982, Annual review of neuroscience.

[24]  Geoffrey E. Hinton,et al.  Separating Figure from Ground with a Parallel Network , 1986, Perception.

[25]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[26]  Daniel L. Alkon,et al.  Memory Traces in the Brain , 1987 .

[27]  E. Kandel,et al.  Differential classical conditioning of a defensive withdrawal reflex in Aplysia californica. , 1983, Science.

[28]  A G Barto,et al.  Learning by statistical cooperation of self-interested neuron-like computing elements. , 1985, Human neurobiology.

[29]  A. Gelperin,et al.  Complex associative learning in small neural networks , 1986, Trends in Neurosciences.

[30]  E. W. Kairiss,et al.  Long-Term Potentiation in Two Synaptic Systems of the Hippocampal Brain Slice , 1989 .

[31]  Gonzalo Viana Di Prisco,et al.  Hebb synaptic plasticity , 1984, Progress in Neurobiology.

[32]  J. Albus A Theory of Cerebellar Function , 1971 .

[33]  E. Kandel,et al.  Is there a cell-biological alphabet for simple forms of learning? , 1984 .

[34]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Richard F. Thompson,et al.  Modeling the Neural Substrates of Associative Learning and Memory: A Computational Approach , 1987 .

[36]  Terrence J. Sejnowski,et al.  The Hebb Rule for Synaptic Plasticity: Algorithms and Implementations , 1989 .

[37]  G. Lynch,et al.  The neurobiology of learning and memory , 1989, Cognition.

[38]  Roman Bek,et al.  Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up , 1978, Kybernetika.

[39]  G Viana Di Prisco Hebb synaptic plasticity. , 1984, Progress in neurobiology.

[40]  S Dehaene,et al.  Spin glass model of learning by selection. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[41]  S. Kelso,et al.  Hebbian synapses in hippocampus. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Masao Ito The Cerebellum And Neural Control , 1984 .

[43]  William B. Levy,et al.  Synaptic modification, neuron selectivity, and nervous system organization , 1985 .

[44]  Tj Sejnowski,et al.  Skeleton filters in the brain , 2014 .

[45]  F. A. Miles,et al.  Plasticity in the vestibulo-ocular reflex: a new hypothesis. , 1981, Annual review of neuroscience.

[46]  W. Levy,et al.  Partial quantification of the associative synaptic learning rule of the dentate gyrus , 1983, Neuroscience.

[47]  A G Barto,et al.  Toward a modern theory of adaptive networks: expectation and prediction. , 1981, Psychological review.

[48]  T. Sejnowski Statistical constraints on synaptic plasticity. , 1977, Journal of theoretical biology.

[49]  S. Sherman,et al.  Organization of visual pathways in normal and visually deprived cats. , 1982, Physiological reviews.

[50]  Klaus Schulten,et al.  Stochastic spin models for pattern recognition , 1987 .

[51]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Terrence J. Sejnowski,et al.  Separating figure from ground with a Boltzmann machine , 1990 .

[53]  Geoffrey E. Hinton,et al.  Parallel visual computation , 1983, Nature.

[54]  B. McNaughton,et al.  Hippocampal synaptic enhancement and information storage within a distributed memory system , 1987, Trends in Neurosciences.

[55]  C. Cohan,et al.  Discriminative behavior and Pavlovian conditioning in the mollusc Pleurobranchaea. , 1986, Journal of neurobiology.

[56]  G. Tesauro A plausible neural circuit for classical conditioning without synaptic plasticity. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[57]  Y. Frégnac,et al.  A cellular analogue of visual cortical plasticity , 1988, Nature.

[58]  R. F. Thompson,et al.  Modeling the neural substrates of associative learning and memory: a computational approach. , 1987, Psychological review.

[59]  T. Sejnowski,et al.  Storing covariance with nonlinearly interacting neurons , 1977, Journal of mathematical biology.

[60]  J.A. Anderson Two models for memory organization using interacting traces , 1970 .

[61]  S. Kelso,et al.  Differential conditioning of associative synaptic enhancement in hippocampal brain slices. , 1986, Science.

[62]  H. Wigström,et al.  On long-lasting potentiation in the hippocampus: a proposed mechanism for its dependence on coincident pre- and postsynaptic activity. , 1985, Acta physiologica Scandinavica.

[63]  J. Hopfield,et al.  Computing with neural circuits: a model. , 1986, Science.

[64]  Richard F. Thompson The neurobiology of learning and memory. , 1986, Science.

[65]  J. Hopfield,et al.  The Logic of Limax Learning , 1985 .