# Computational models of learning and synaptic plasticity

arXiv: https://arxiv.org/abs/2412.05501 · HTML: https://mlsanity.com/paper/2412.05501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Engram Memory Encoding and Retrieval: A Neurocomputational Perspective](https://mlsanity.com/paper/2506.01659/) (2025)

## Abstract (arXiv)

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.