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Complementary approaches to Synaptic Plasticity: Objective Functions and Biophysics

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Two complementary approaches for the modelling of synaptic plasticity of rate encoding and spiking neurons respectively, are presented. For rate encoding neurons, a novel objective function and the resulting self-limiting Hebbian learning rules are discussed and compared with traditional models like Oja’s rule. We find that the new rules optimize non-Gaussianity, leading naturally to the tendency to perform an independent component analysis. For spiking neurons, a simplistic, though biologically motivated two-trace model for STDP is presented aiming to bridge the worlds of detailed biophysical models and simple phenomenological rules. The model is shown to reproduce traditional pairwise STDP results, as well as triplet protocols. Finally, frequency effects are studied numerically.

This talk is part of the Computational Neuroscience series.

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