Delayed-nonmatch-to-sample (DNMS) task is memory-dependent. Hippocampal CA3 and CA1 cells were shown to be encoding the required spatial and temporal information to complete this task. In order to identify possible changes in neural population nonlinear dynamics during learning of the DNMS task, we have first modeled the input-output transformation of spike trains across brain subregions from learning animals using a multiple-input, multiple-output (MIMO) nonlinear dynamic model. The feedforward and feedback kernels describing the relations between hippocampal CA3 and CA1 subregions have shown significant changes at different training sessions.
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