Architecture of attractor neural networks performing cognitive fast scanning

The salient features of the findings of high-speed scanning experiments of the Sternberg type and several attempts to account for them are reviewed, as well as the challenge implied by these findings for the general approach of attractor neural networks (ANNS).We formulate a detailed, biologically flavoured, neural network, composed of three sub-networks: one preserving the test stimulus (probe), one encoding the memory set, and one encoding decision elements. Each network is a fully fed-back ANN, and serves basically to classify its inputs into classes of those inputs that lead quickly to attractors, i.e. states in which a well defined set of neurons emits bursts of spikes, and those inputs which do not. The other role of the ANN is to be able to preserve the results of the classifications for extended times. The networks communicate between themselves only when they arrive at attractors, i.e. only bursts are communicated between sub-networks. The conceptual division of the single network into sub-networ...

[1]  J. Buhmann,et al.  Associative memory with high information content. , 1989, Physical review. A, General physics.

[2]  M. W. Kristofferson.  When item recognition and visual search functions are similar , 1972 .

[3]  I. Guyon,et al.  Information storage and retrieval in spin-glass like neural networks , 1985 .

[4]  D Kleinfeld,et al.  Sequential state generation by model neural networks. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[5]  L. Standing.  Learning 10000 pictures , 1973 .

[6]  D. Amit,et al.  Statistical mechanics of neural networks near saturation , 1987 .

[7]  B. L. Lively.  Speed/accuracy trade off and practice as determinants of stage durations in a memory-search task. , 1972 .

[8]  M. Tsodyks,et al.  The Enhanced Storage Capacity in Neural Networks with Low Activity Level , 1988 .

[9]  R. Andersen,et al.  The influence of the angle of gaze upon the excitability of the light- sensitive neurons of the posterior parietal cortex , 1983, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[10]  James T. Townsend,et al.  A note on the identifiability of parallel and serial processes , 1971 .

[11]  S. Sternberg.  High-Speed Scanning in Human Memory , 1966, Science.

[12]  J. Theios,et al.  Memory scanning as a serial self-terminating process , 1973 .

[13]  M. Manosevitz,et al.  High-Speed Scanning in Human Memory , 2022 .

[14]  B. L. Lively,et al.  The use of category information in a memory-search task , 1972 .

[15]  John Ross,et al.  Extended practice with a single-character classification task , 1970 .

[16]  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.

[17]  Haim Sompolinsky,et al.  The Theory of Neural Networks: The Hebb Rule and Beyond , 1987 .

[18]  S. Sternberg.  Memory-scanning: mental processes revealed by reaction-time experiments. , 1969, American scientist.

[19]  G. Parisi,et al.  Asymmetric neural networks and the process of learning , 1986 .

[20]  B. Katz,et al.  The effect of inhibitory nerve impulses on a crustacean muscle fibre , 1953, The Journal of physiology.

[21]  Sompolinsky,et al.  Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

[22]  S. Sternberg.  Memory Scanning: New Findings and Current Controversies , 1975 .

[23]  J. P. Cavanagh.  Relation between the immediate memory span and the memory search rate. , 1972 .

[24]  D. Amit,et al.  Spin-glass models of neural networks , 1987 .

[25]  Alan D. Baddeley,et al.  Reaction Time and Short-term Memory: Implications of Repetition Effects for the High-speed Exhaustive Scan Hypothesis * , 1973 .

[26]  Sompolinsky,et al.  Neural networks with nonlinear synapses and a static noise. , 1986, Physical review. A, General physics.

[27]  Teuvo Kohonen,et al.  Representation of Associated Data by Matrix Operators , 1973, IEEE Transactions on Computers.

[28]  E. Gardner.  The space of interactions in neural network models , 1988 .

[29]  C. Clifton,et al.  Effects of serial position and delay of probe in a memory scan task. , 1970 .

[30]  D J Amit,et al.  Neural networks counting chimes. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

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

[32]  M. W. Kristofferson.  Effects of practice on character-classification performance. , 1972 .

[33]  Richard C. Atkinson,et al.  Search and decision processes in recognition memory. , 1974 .