Abstract
Adding synaptic modification to the inhibitory interneuron in a minimal computational model of hippocampal region CA3 improves average performance of the simulations. After training on two partially overlapping sequences, simulations are tested on a sequence completion problem that can only be solved by using context dependent information. Simulations with dynamic autonomously scaling (DAS) inhibition are more robust than those without. In the DAS model, scaling factors for inhibition are adjusted gradually over time to compensate for the original model's tendency to move away from a pre-set activity level. This variable inhibition modifies more slowly than the local, associative synaptic modification of the excitatory synapses. As a result, activity fluctuations from one time-step to the next continue to occur, but average activity levels show small variability across training. These results suggest that restricting long term activity fluctuations can be beneficial to recurrent networks that must learn context dependent sequences.
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