Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation

Biological networks are capable of gradual learning based on observing a large number of exemplars over time as well as of rapidly memorizing specific events as a result of a single exposure. The focus of research in neural networks has been on gradual learning, and the modeling of one-shot memorization has received relatively little attention. Nevertheless, the development of biologically plausible computational models of rapid memorization is of considerable value, since such models would enhance our understanding of the neural processes underlying episodic memory formation. A few researchers have attempted the computational modeling of rapid (one-shot) learning within a framework described variably as recruitment learning and vicinal algorithms. Here it is shown that recruitment learning and vicinal algorithms can be grounded in the biological phenomena of long-term potentiation and long-term depression. Toward this end, a computational abstraction of LTP and LTD is presented, and an "algorithm" for the recruitment of binding-detector (or coincidence-detector) cells is described and evaluated using biologically realistic data.

[1]  C. von der Malsburg,et al.  Am I Thinking Assemblies , 1986 .

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

[3]  R. Passingham The hippocampus as a cognitive map J. O'Keefe & L. Nadel, Oxford University Press, Oxford (1978). 570 pp., £25.00 , 1979, Neuroscience.

[4]  Lokendra Shastri,et al.  Recruitment of binding and binding-error detector circuits via long-term potentiation , 1999, Neurocomputing.

[5]  T. Bliss,et al.  Long‐lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path , 1973, The Journal of physiology.

[6]  D. Amaral,et al.  Neurons, numbers and the hippocampal network. , 1990, Progress in brain research.

[7]  L. Shastri,et al.  From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony , 1993, Behavioral and Brain Sciences.

[8]  Jerome A. Feldman,et al.  Extending Embodied Lexical Development , 1998 .

[9]  T. Sejnowski,et al.  Associative long-term depression in the hippocampus induced by hebbian covariance , 1989, Nature.

[10]  L. Squire Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans. , 1992, Psychological review.

[11]  Wolfgang Maass,et al.  On Computation with Pulses , 2000, Electron. Colloquium Comput. Complex..

[12]  J. Donoghue,et al.  Learning-induced LTP in neocortex. , 2000, Science.

[13]  W. Singer Synchronization of cortical activity and its putative role in information processing and learning. , 1993, Annual review of physiology.

[14]  Lokendra Shastri,et al.  A Biological Grounding of Recruitment Learning and Vicinal Algorithms , 1999 .

[15]  R. Nicoll,et al.  Contrasting properties of two forms of long-term potentiation in the hippocampus , 1995, Nature.

[16]  R. Nicoll,et al.  S CIENCE ’ S C OMPASS ● REVIEW REVIEW: NEUROSCIENCE Long-Term Potentiation—A Decade of Progress? , 2022 .

[17]  W. Levy,et al.  Synapses as associative memory elements in the hippocampal formation , 1979, Brain Research.

[18]  John A. Barnden,et al.  Semantic Networks , 1998, Encyclopedia of Social Network Analysis and Mining.

[19]  E. W. Kairiss,et al.  Hebbian synapses: biophysical mechanisms and algorithms. , 1990, Annual review of neuroscience.

[20]  Joachim Diederich,et al.  Instruction and High-level Learning in Connectionist Networks , 1989 .

[21]  G. V. Hoesen,et al.  The parahippocampal gyrus: New observations regarding its cortical connections in the monkey , 1982, Trends in Neurosciences.

[22]  E. Rolls,et al.  Computational analysis of the role of the hippocampus in memory , 1994, Hippocampus.

[23]  SM Dudek,et al.  Bidirectional long-term modification of synaptic effectiveness in the adult and immature hippocampus , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[24]  David Bailey,et al.  Layered Hybrid Connectionist Models for Cognitive Science , 1998, Hybrid Neural Systems.

[25]  Lokendra Shastri,et al.  Seeking coherent explanations -- a fusion of structured connectionism, temporal synchrony, and evident reasoning - eScholarship , 2000 .

[26]  D. Linden,et al.  Long-term synaptic depression in the mammalian brain , 1994, Neuron.

[27]  M J West,et al.  Stereological studies of the hippocampus: a comparison of the hippocampal subdivisions of diverse species including hedgehogs, laboratory rodents, wild mice and men. , 1990, Progress in brain research.

[28]  H. Eichenbaum,et al.  Memory, amnesia, and the hippocampal system , 1993 .

[29]  E. Shimizu,et al.  Genetic enhancement of learning and memory in mice , 1999, Nature.

[30]  W. Singer,et al.  Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation , 1993, Trends in Neurosciences.

[31]  Lokendra Shastri,et al.  Semantic Networks: An Evidential Formalization and Its Connectionist Realization , 1988 .

[32]  Lokendra Shastri,et al.  Rules and Variables in Neural Nets , 1991, Neural Computation.

[33]  Lokendra Shastri,et al.  A Connectionist Encoding of Schemas and Reactive Plans , 1997 .

[34]  E. Tulving Elements of episodic memory , 1983 .

[35]  Leslie G. Valiant,et al.  Circuits of the mind , 1994 .

[36]  D Marr,et al.  Simple memory: a theory for archicortex. , 1971, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[37]  M. Page,et al.  Connectionist modelling in psychology: A localist manifesto , 2000, Behavioral and Brain Sciences.