Multiplicity of Stable Attractors in Disordered Neural Models
We show how large-deviation statistics allows one to obtain reliable estimates of the multiplicity of stable fixed-points in a model of neural ordinary differential equations previously employed in computational tasks. The result is obtained by developing a suitable perturbative method in the amplitude of the disorder. It turns out that for not-too-large coupling strengths there are no qualitative differences between the symmetric case, when the dynamics is a purely gradient evolution, and the asymmetric case, when limit cycles and chaos can, in principle, arise. The selection of this specific model is dictated by pedagogical reasons, but we are confident that the approach can be extended...
| Authors | Raffaele Marino, Roberto Livi, Antonio Politi |
|---|---|
| Primary category | cond-mat.dis-nn |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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