STDP as presynaptic activity times rate of change of postsynaptic activity

TL;DR

This paper proposes a novel synaptic weight update rule based solely on firing rates and their derivatives, aligning with STDP principles without requiring spike timing, and linking to gradient descent in neural learning.

Contribution

Introduces a rate-based STDP rule expressed through firing rates and their derivatives, bridging biological observations with machine learning theory.

Findings

The rule aligns with biological STDP observations.

It models synaptic changes as a form of stochastic gradient descent.

Applicable for theoretical analysis of neural learning mechanisms.

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.