Learning in Willshaw-type associative networks.

<p><b>A</b>, Memory storage by Hebbian weight plasticity (Eq. 5) in a fully connected network (). Address patterns are associated to content patterns where (here ). Each memory is represented by a binary activity vector of length having active units (which define the corresponding cell assembly). <b>B</b>, One-step retrieval of the first memory from a noisy query pattern having two of the four active units in (). Here can perfectly reactivate the corresponding memory pattern in population () applying a firing threshold on dendritic potentials . <b>C</b>, As a simple form of structural plasticity, silent synapses can be pruned <i>after</i> learning. The resulting network has only 28 (instead of 49) synapses corresponding to a lower anatomical connectivity , whereas the effectual connectivity is still . Thus, pruning does not change network function, but increases stored information per synapse. <b>D</b>, Ongoing structural plasticity can similarly increase storage capacity during more realistic learning in networks with low anatomical connectivity (here ). During each time step , Hebbian weight plasticity potentiates and consolidates synapses with non-zero consolidation signal (which equals of panel A), whereas the remaining silent synapses are eliminated and replaced by new synapses at random locations. Note that the resulting network at is the same as in panel C.