Structural plasticity, cortical memory, and the spacing effect

By Andreas Knoblauch. 2009

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The neurophysiological basis of learning and memory is commonly attributed to the modification of synaptic strengths in neuronal networks. Recent experiments suggest also a major role of structural plasticity, including elimination and regeneration of synapses, growth and retraction of dendritic spines, and remodeling of axons and dendrites [1]. Here, I develop a simple model of structural plasticity and synaptic consolidation in neural networks and apply it to Willshaw-type network models of distributed associative memory [2]. The model assumes synapses with discrete weight states. Synapses with low weights have a high probability of being erased and replaced by novel synapses at other locations. In contrast, synapses with large weights are consolidated and cannot be erased. Analysis and numerical simulations reveal that this model can explain various cognitive phenomena much better than alternative network models employing synaptic plasticity only.

Subfields: Cognitive Neuroscience