KIP publications

 
year 2019
author(s) Andreas Baumbach, Johannes Schemmel, Mihai Petrovici
title Magnetic phenomena in ensembles of spiking neurons
KIP-Nummer HD-KIP 19-120
KIP-Gruppe(n) F9
document type Paper
Keywords sampling, spiking neurons, Ising model, phase transitions
source Bernstein Conference - Berlin, 2019
doi 10.12751/nncn.bc2019.0240
Abstract (en)

In this work we go back to the original and arguably simplest model known to exhibit critical phenomena, the Ising model for ferromagnetism. Following the model of [Petrovici 2016], we implement an Ising-like spiking neural network that implements the same Boltzmann distribution using recent work on spiking neural networks under Poissonian noise. As such a description is widely used in neuroscience to effectively describe biological models and data one would expect that all the phenomena known from statistical physics can also be observed in these systems.

This work investigates a simplified model, the neural sampling framework introduced by [Buesing 2011], which we modify to include exponentially decaying interactions (resembling biological interactions), capturing the major dynamical difference to LIF sampling. While the global properties, like the unordered phase in the infinite temperature limit and the ordered phase in the zero temperature limit can still be observed. We show that the phase diagram of this model shows richer phenomena than the classical Ising model predicts. For example it allows such a system to converge non-monotonously to its final state even for a non-zero external field.

bibtex
@article{baumbach2019Magnetic,
  author   = {Baumbach, Andreas & Schemmel, Johannes & Petrovici, Mihai A.},
  title    = {Magnetic phenomena in ensembles of spiking neurons},
  journal  = {Bernstein Conference 2019},
  year     = {2019},
  volume   = {},
  pages    = {},
  doi      = {10.12751/nncn.bc2019.0240},
  url      = {https://abstracts.g-node.org/conference/BC19/abstracts#/uuid/6d315426-2ff7-44ff-9328-1967f3072148}
}
Datei pdf
URL Bernstein Abstract
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