Learning as filtering: Implications for spike-based plasticity.

Jegminat, Jannes; Surace, Simone Carlo; Pfister, Jean-Pascal (2022). Learning as filtering: Implications for spike-based plasticity. PLoS computational biology, 18(2), e1009721. Public Library of Science 10.1371/journal.pcbi.1009721

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Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time-varying environment during the learning process and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network-the Synaptic Filter-and show its computational and biological relevance. For the computational relevance, we show that filtering improves the weight estimation performance compared to a gradient learning rule with optimal learning rate. The dynamics of the mean of the Synaptic Filter is consistent with spike-timing dependent plasticity (STDP) while the dynamics of the variance makes novel predictions regarding spike-timing dependent changes of EPSP variability. Moreover, the Synaptic Filter explains experimentally observed negative correlations between homo- and heterosynaptic plasticity.

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