Sequential Memory: A Putative Neural and Synaptic Dynamical Mechanism

A key issue in the neurophysiology of cognition is the problem of sequential learning. Sequential learning refers to the ability to encode and represent the temporal order of discrete elements occurring in a sequence. We show that the short-term memory for a sequence of items can be implemented in an autoassociation neural network. Each item is one of the attractor states of the network. The autoassociation network is implemented at the level of integrate-and-fire neurons so that the contributions of different biophysical mechanisms to sequence learning can be investigated. It is shown that if it is a property of the synapses or neurons that support each attractor state that they adapt, then everytime the network is made quiescent (e.g., by inhibition), then the attractor state that emerges next is the next item in the sequence. We show with numerical simulations implementations of the mechanisms using (1) a sodium inactivation-based spike-frequency-adaptation mechanism, (2) a Ca2+-activated K+ current, and (3) short-term synaptic depression, with sequences of up to three items. The network does not need repeated training on a particular sequence and will repeat the items in the order that they were last presented. The time between the items in a sequence is not fixed, allowing the items to be read out as required over a period of up to many seconds. The network thus uses adaptation rather than associative synaptic modification to recall the order of the items in a recently presented sequence.

[1]  S. Grillner,et al.  Intrinsic function of a neuronal network — a vertebrate central pattern generator 1 Published on the World Wide Web on 8 April 1998. 1 , 1998, Brain Research Reviews.

[2]  L. A. Jeffress,et al.  Cerebral Mechanisms in Behavior , 1953 .

[3]  S. Grillner,et al.  Neuronal network generating locomotor behavior in lamprey: circuitry, transmitters, membrane properties, and simulation. , 1991, Annual review of neuroscience.

[4]  D. Prince,et al.  GABAA receptor‐mediated currents in interneurons and pyramidal cells of rat visual cortex , 1998, The Journal of physiology.

[5]  N. Spruston,et al.  Dendritic glutamate receptor channels in rat hippocampal CA3 and CA1 pyramidal neurons. , 1995, The Journal of physiology.

[6]  C. Stevens,et al.  Voltage dependence of NMDA-activated macroscopic conductances predicted by single-channel kinetics , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[7]  W. Senn,et al.  Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. , 2003, Journal of neurophysiology.

[8]  Alessandro Treves,et al.  Stable and Rapid Recurrent Processing in Realistic Autoassociative Memories , 1998, Neural Computation.

[9]  Xiao-Jing Wang,et al.  Spike-Frequency Adaptation of a Generalized Leaky Integrate-and-Fire Model Neuron , 2004, Journal of Computational Neuroscience.

[10]  D. Prince,et al.  GABA A receptor-mediated currents in interneurons and pyramidal cells of rat visual cortex , 1998 .

[11]  G. Deco,et al.  Top-down selective visual attention: A neurodynamical approach , 2001 .

[12]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[13]  A. Chapanis.  Cerebral Mechanisms in Behavior.Lloyd A. Jeffress , 1953 .

[14]  E. Rolls,et al.  Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex , 2003, The European journal of neuroscience.

[15]  E. Rolls,et al.  Attention, short-term memory, and action selection: A unifying theory , 2005, Progress in Neurobiology.

[16]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[17]  Bruno B Averbeck,et al.  Parallel processing of serial movements in prefrontal cortex , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[18]  E. Rolls,et al.  Neural networks and brain function , 1998 .

[19]  E. Rolls,et al.  Synaptic and spiking dynamics underlying reward reversal in the orbitofrontal cortex. , 2004, Cerebral cortex.

[20]  M. Petrides.  Functional specialization within the dorsolateral frontal cortex for serial order memory , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[21]  D. McCormick,et al.  Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. , 1985, Journal of neurophysiology.

[22]  Morten H. Christiansen,et al.  Sequential learning in non-human primates , 2001, Trends in Cognitive Sciences.

[23]  Gustavo Deco,et al.  Large-scale neural model for visual attention: integration of experimental single-cell and fMRI data. , 2002, Cerebral cortex.

[24]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[25]  P S Goldman-Rakic,et al.  Functional synergism between putative gamma-aminobutyrate-containing neurons and pyramidal neurons in prefrontal cortex. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Joachim M. Buhmann,et al.  Noise-driven temporal association in neural networks , 1987 .

[27]  P. Goldman-Rakic,et al.  Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task. , 2003, Journal of neurophysiology.

[28]  Allen I. Selverston,et al.  Model Neural Networks and Behavior , 1985, Springer US.

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

[30]  A. Luria.  Higher Cortical Functions in Man , 1980, Springer US.

[31]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[32]  Paul Antoine Salin,et al.  Spontaneous GABAA receptor-mediated inhibitory currents in adult rat somatosensory cortex. , 1996, Journal of neurophysiology.

[33]  Gustavo Deco,et al.  Computational neuroscience of vision , 2002 .

[34]  G. Dell,et al.  Language production and serial order: a functional analysis and a model. , 1997, Psychological review.

[35]  Michele Giugliano,et al.  Non-monotonic Current-to-Rate Response Function in a Novel Integrate-and-Fire Model Neuron , 2002, ICANN.

[36]  L. Abbott,et al.  Model of song selectivity and sequence generation in area HVc of the songbird. , 2003, Journal of neurophysiology.

[37]  A. Destexhe.  Kinetic Models of Synaptic Transmission , 1997 .

[38]  D. Amit,et al.  Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex. , 1997, Cerebral cortex.

[39]  R. Nicoll,et al.  Mechanisms generating the time course of dual component excitatory synaptic currents recorded in hippocampal slices , 1990, Neuron.

[40]  Germán Mato,et al.  On Numerical Simulations of Integrate-and-Fire Neural Networks , 1998, Neural Computation.

[41]  E. Rolls,et al.  A computational theory of hippocampal function, and empirical tests of the theory , 2006, Progress in Neurobiology.

[42]  Kanter,et al.  Temporal association in asymmetric neural networks. , 1986, Physical review letters.

[43]  Tai Sing Lee,et al.  A unified model of spatial and object attention based on inter-cortical biased competition , 2002, Neurocomputing.

[44]  Teuvo Kohonen,et al.  Associative memory. A system-theoretical approach , 1977 .

[45]  A Berthoz,et al.  A neural network model of sensoritopic maps with predictive short-term memory properties. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[46]  Joaquín M. Fuster,et al.  Executive frontal functions , 2000, Experimental Brain Research.