Statistical Mechanics of Recurrent Neural Networks II. Dynamics
A.C.C. Coolen
TL;DR
This paper reviews the non-equilibrium statistical mechanics of recurrent neural networks with various neuron models, focusing on their dynamic behavior and how they evolve over time.
Contribution
It provides a comprehensive lecture notes style review of the dynamics of recurrent neural networks, complementing a previous static analysis, and covers both discrete and continuous neuron models.
Findings
Detailed analysis of neural network dynamics
Comparison of different neuron models
Insights into non-equilibrium behavior
Abstract
A lecture notes style review of the non-equilibrium statistical mechanics of recurrent neural networks with discrete and continuous neurons (e.g. Ising, graded-response, coupled-oscillators). To be published in the Handbook of Biological Physics (North-Holland). Accompanied by a similar review (part I) dealing with the statics.
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Taxonomy
TopicsNeural Networks and Applications · stochastic dynamics and bifurcation · Neural dynamics and brain function