The Effect of Graph Connecitivity on Metastability on a Stochastic System of Spiking Neurons
Morgan Andr\'e, L\'eo Planche

TL;DR
This paper investigates how the connectivity structure of a stochastic spiking neuron network influences its metastability, revealing different behaviors depending on the graph type and leakage parameter.
Contribution
It demonstrates that the metastability properties depend on the graph structure and leakage parameter, showing phase transition behavior and extending previous results to new graph configurations.
Findings
For gamma > 1, the extinction time becomes deterministic in finite 1D lattice.
Metastability holds for all positive gamma on complete graphs.
Different graph structures lead to distinct metastability behaviors.
Abstract
We consider a continuous-time stochastic model of spiking neurons. In this model, we have a finite or countable number of neurons which are vertices in some graph where the edges indicate the synaptic connection between them. We focus on metastability, understood as the property for the time of extinction of the network to be asymptotically memory-less, and we prove that this model exhibits two different behaviors depending on the nature of the specific underlying graph of interaction that is chosen. This model depends on a leakage parameter , and it was previously proven that when the graph is the infinite one-dimensional lattice, this model presents a phase transition with respect to . It was also proven that, when is small enough, the renormalized time of extinction (the first time at which all neurons have a null membrane potential) of a finite…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
