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Optimal Recall from Bounded Metaplastic Synapses: Predicting Functional Adaptations in Hippocampal Area CA3

Figure 1

Autoassociative memory with bounded synapses.

A. Memories are stored in the recurrent collaterals of a neural network. Five example synapses are shown, each in a different state (colors from panel C). B. During storage, a sequence of items, ( indexes time backwards from the time of recall), induces changes to the internal states, , and thus to the overt efficacies, , of recurrent synapses in the network. During retrieval, the dynamics of the network should identify the pattern to be recalled given a cue and information in the synaptic efficacies. C. The cascade model of synaptic metaplasticity [17]. Colored circles are latent states, , that correspond to two different synaptic efficacies, ; arrows are state transitions (blue: depression, red: potentiation). Tables show different variants of mapping pre- and post-synaptic activations to depression (D) and potentiation (P) under the pre- and postsynaptically-gated learning rules. D. Left: the evolution of the expected distribution over synaptic states (thickness of stripes is proportional to the probability of the corresponding state, see panel C for color code) after a potentiation event at time (marked by the vertical arrow) and the storage of random patterns in subsequent steps, and the distribution of times at which this memory may need to be recalled (white curve). Middle: the time-averaged expected distribution of hidden synaptic states at the unknown time of recall of this memory. Right: the corresponding distribution over overt synaptic efficacies.

doi: https://doi.org/10.1371/journal.pcbi.1003489.g001