Event-based update of synapses in voltage-based learning rules

TL;DR

This paper introduces two efficient event-based algorithms for synaptic plasticity that incorporate postsynaptic membrane potentials, significantly improving simulation scalability and performance in large neural networks.

Contribution

The authors develop and compare two novel event-based algorithms for archiving membrane potentials, compatible with modern simulators, enhancing efficiency over traditional time-driven methods.

Findings

Event-based algorithms outperform time-driven schemes in memory and computation.

Compression and sampling of membrane potential data further improve performance.

Algorithms enable scalable simulation of voltage-based plasticity in large networks.

Abstract

Due to the point-like nature of neuronal spiking, efficient neural network simulators often employ event-based simulation schemes for synapses. Yet many types of synaptic plasticity rely on the membrane potential of the postsynaptic cell as a third factor in addition to pre- and postsynaptic spike times. Synapses therefore require continuous information to update their strength which a priori necessitates a continuous update in a time-driven manner. The latter hinders scaling of simulations to realistic cortical network sizes and relevant time scales for learning. Here, we derive two efficient algorithms for archiving postsynaptic membrane potentials, both compatible with modern simulation engines based on event-based synapse updates. We theoretically contrast the two algorithms with a time-driven synapse update scheme to analyze advantages in terms of memory and computations. We further present a reference implementation in the spiking neural network simulator NEST for two prototypical voltage-based plasticity rules: the Clopath rule and the Urbanczik-Senn rule. For both rules, the two event-based algorithms significantly outperform the time-driven scheme. Depending on the amount of data to be stored for plasticity, which heavily differs between the rules, a strong performance increase can be achieved by compressing or sampling of information on membrane potentials. Our results on computational efficiency related to archiving of information provide guidelines for the design of learning rules in order to make them practically usable in large-scale networks.