Computational models of learning and synaptic plasticity

TL;DR

This paper reviews various computational models of synaptic plasticity and learning paradigms, highlighting their roles in explaining biological learning processes and their potential connections to observed behaviors.

Contribution

It provides a comprehensive overview of synaptic plasticity models and their application to different learning paradigms, emphasizing the diversity and complexity of biological learning mechanisms.

Findings

Different models explain diverse plasticity phenomena

Synaptic models relate to Pavlovian and motor learning

Computational models support linking synaptic changes to learning outcomes

Abstract

Many mathematical models of synaptic plasticity have been proposed to explain the diversity of plasticity phenomena observed in biological organisms. These models range from simple interpretations of Hebb's postulate, which suggests that correlated neural activity leads to increases in synaptic strength, to more complex rules that allow bidirectional synaptic updates, ensure stability, or incorporate additional signals like reward or error. At the same time, a range of learning paradigms can be observed behaviorally, from Pavlovian conditioning to motor learning and memory recall. Although it is difficult to directly link synaptic updates to learning outcomes experimentally, computational models provide a valuable tool for building evidence of this connection. In this chapter, we discuss several fundamental learning paradigms, along with the synaptic plasticity rules that might be used to implement them.