Neural coding for the retrieval of multiple memory patterns.

We investigate the retrieval dynamics in a feature-based semantic memory model, in which the features are coded by neurons of the Hindmarsh-Rose type in the chaotic regime. We consider the retrieval process as consisting of the synchronized firing activity of the neurons coding for the same memory pattern. The retrieval dynamics is investigated for multiple patterns, with particular attention to the case of overlapping memories. In this case, we hypothesize a dynamical nontransitive mechanism based on synchronization, that allows for a shared feature to participate in multiple memory representations. The problem of the choice of a cognitive plausible time-scale for the retrieval analysis is investigated by analyzing the information that can be inferred from finite-time analyses. Different types of indicators are proposed in order to evaluate the temporal dynamics of the neurons engaged in the retrieval process. We interpret the simulation results as suggestive of a role for chaotic dynamics in allowing for flexible composition of elementary meaningful units in memory representations.

[1]  C. Gray The Temporal Correlation Hypothesis of Visual Feature Integration Still Alive and Well , 1999, Neuron.

[2]  Antonino Raffone,et al.  Dynamic synchronization and chaos in an associative neural network with multiple active memories. , 2003, Chaos.

[3]  David E. Rumelhart,et al.  Brain style computation: learning and generalization , 1990 .

[4]  S. Thorpe,et al.  The Time Course of Visual Processing: From Early Perception to Decision-Making , 2001, Journal of Cognitive Neuroscience.

[5]  Alessandro Treves,et al.  Frontal latching networks: a possible neural basis for infinite recursion , 2005, Cognitive neuropsychology.

[6]  Norbert Krüger,et al.  Three Dilemmas of Signal- and Symbol-Based Representations in Computer Vision , 2005, BVAI.

[7]  Joel L. Davis,et al.  An Introduction to Neural and Electronic Networks , 1995 .

[8]  J. Hindmarsh,et al.  A model of neuronal bursting using three coupled first order differential equations , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[9]  C. von der Malsburg The what and why of binding: the modeler's perspective. , 1999, Neuron.

[10]  Tim Shallice,et al.  Fractionation of the supervisory system. , 2002 .

[11]  Fortunato Tito Arecchi,et al.  A Feature-Based Model of Semantic Memory: The Importance of Being Chaotic , 2005, BVAI.

[12]  R. Segev,et al.  Long term behavior of lithographically prepared in vitro neuronal networks. , 2002, Physical review letters.

[13]  R Quian Quiroga,et al.  Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  D. Stuss,et al.  Principles of frontal lobe function , 2002 .

[15]  W. Singer,et al.  Temporal coding in the visual cortex: new vistas on integration in the nervous system , 1992, Trends in Neurosciences.

[16]  A. Antal,et al.  Corticostriatal circuitry mediates fast-track visual categorization. , 2002, Brain research. Cognitive brain research.

[17]  L. Saksida,et al.  Impairments in visual discrimination after perirhinal cortex lesions: testing ‘declarative’ vs. ‘perceptual‐mnemonic’ views of perirhinal cortex function , 2003, The European journal of neuroscience.

[18]  M. D’Esposito Working memory. , 2008, Handbook of clinical neurology.

[19]  Daniel J. Amit Modeling brain function: Introduction , 1989 .

[20]  David C. Plaut,et al.  Semantic and Associative Priming in a Distributed Attractor Network , 1995 .

[21]  Richard Hans Robert Hahnloser,et al.  An ultra-sparse code underliesthe generation of neural sequences in a songbird , 2002, Nature.