STDP as presynaptic activity times rate of change of postsynaptic activity

Abstract

We introduce a weight update formula that is expressed only in terms of firing rates and their derivatives and that results in changes consistent with those associated with spike-timing dependent plasticity (STDP) rules and biological observations, even though the explicit timing of spikes is not needed. The new rule changes a synaptic weight in proportion to the product of the presynaptic firing rate and the temporal rate of change of activity on the postsynaptic side. These quantities are interesting for studying theoretical explanation for synaptic changes from a machine learning perspective. In particular, if neural dynamics moved neural activity towards reducing some objective function, then this STDP rule would correspond to stochastic gradient descent on that objective function.

References in corpus (2)

• Gradient learning in spiking neural networks by dynamic perturbation of conductances
• Early Inference in Energy-Based Models Approximates Back-Propagation

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• Learning binary or real-valued time-series via spike-timing dependent plasticity
• Contrastive Similarity Matching for Supervised Learning
• In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor