Capacity-Enhancing Synaptic Learning Rules in a Medial Temporal Lobe Online Learning Model

Abstract

Medial temporal lobe structures are responsible for recording the continuous stream of autobiographical memories that define our unique personal history. Remarkably, these areas can construct durable memories from brief exposures to the constantly changing activity patterns arriving from antecedent cortical areas. Using a computer model of the hippocampal Schaffer collateral pathway that incorporates evidence for dendritic spikes in CA1 pyramidal neurons, we searched for biologically-plausible long-term potentiation (LTP) and homeostatic depression (HD) rules that maximize "online" learning capacity. We found memory utilization is most efficient when (1) very few synapses are modified to store each pattern, (2) LTP, the learning operation, is dendrite-specific and gated by distinct pre- and postsynaptic thresholds, (3) HD, the forgetting operation, co-occurs with LTP and targets least-recently potentiated synapses, and (4) both LTP and HD are all-or-none, leading de facto to binary-valued synaptic weights. In networks containing up to 40 million synapses, the learning scheme led to order-of-magnitude capacity increases compared to conventional plasticity rules.