Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory |
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Authors: | Email author" target="_blank">Anders?LansnerEmail author Erik?Fransén Anders?Sandberg |
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Institution: | (1) Studies of Artificial Neural Systems (SANS), Dept. of Numerical Analysis and Computer Science, Royal Institute of Technology, Stockholm, Sweden |
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Abstract: | Summary During the last few decades we have seen a convergence among ideas and hypotheses regarding functional principles underlying
human memory. Hebb’s now more than fifty years old conjecture concerning synaptic plasticity and cell assemblies, formalized
mathematically as attractor neural networks, has remained among the most viable and productive theoretical frameworks. It
suggests plausible explanations for Gestalt aspects of active memory like perceptual completion, reconstruction and rivalry.
We review the biological plausibility of these theories and discuss some critical issues concerning their associative memory
functionality in the light of simulation studies of models with palimpsest memory properties. The focus is on memory properties
and dynamics of networks modularized in terms of cortical minicolumns and hypercolumns. Biophysical compartmental models demonstrate
attractor dynamics that support cell assembly operations with fast convergence and low firing rates. Using a scaling model
we obtain reasonable relative connection densities and amplitudes. An abstract attractor network model reproduces systems
level psychological phenomena seen in human memory experiments as the Sternberg and von Restorff effects.
We conclude that there is today considerable substance in Hebb’s theory of cell assemblies and its attractor network formulations,
and that they have contributed to increasing our understanding of cortical associative memory function.
The criticism raised with regard to biological and psychological plausibility as well as low storage capacity, slow retrieval
etc has largely been disproved. Rather, this paradigm has gained further support from new experimental data as well as computational
modeling. |
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Keywords: | Biophysical compartmental neuron model hypercolumns minicolumns forgetting incremental learning reaction time |
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