Abstract
This chapter studies the bridges and differences between the statistical physics of disordered systems, as developed notably in the context of spin glass theory, and problems in neuroscience. In a first contribution (Sec. 25.1), Nicolas Brunel, Rémi Monasson and Haim Sompolinsky first recall the main lines of the statistical physics approach to neural networks models as developed in the 1980s and 1990s. They then survey more recent developments at the interface between statistical physics and neuroscience, including the inference of synaptic plasticity rules and the statistics of synaptic connectivity. Finally they present the Tempotron model for learning temporal patterns. In a second contribution (Sec. 25.2), Leo van Hemmen discusses the difference between real spin glasses, neuronal networks (of real biological neurons) and neural networks (of artificial neurons); with illustrations ranging from site-disorder models of spin glasses to temporal coding in neuronal networks and unlearning.
Original language | English |
---|---|
Title of host publication | Spin Glass Theory and Far Beyond |
Subtitle of host publication | Replica Symmetry Breaking after 40 Years |
Publisher | World Scientific Publishing Co. |
Pages | 499-521 |
Number of pages | 23 |
ISBN (Electronic) | 9789811273926 |
ISBN (Print) | 9789811273919 |
DOIs | |
State | Published - 1 Jan 2023 |
Bibliographical note
Publisher Copyright:© 2023 World Scientific Publishing.