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Stochasticity from function - Why the Bayesian brain may need no noise.

Dominik Dold1, Ilja Bytschok2, Akos F Kungl1

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Neural Networks : the Official Journal of the International Neural Network Society
|August 27, 2019
PubMed
Summary
This summary is machine-generated.

Spiking neural networks can perform Bayesian inference without explicit noise. Correlations in neural activity are shaped by synaptic plasticity, enabling deterministic networks to compute probabilistically.

Keywords:
Generative and discriminative modelsNeuromorphic hardwareNoise and stochasticityProbabilistic computingSpiking networks

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neuroscience

Background:

  • Brain activity variability is increasingly viewed as a mechanism for probabilistic computing, not just noise.
  • Current models often assume explicit noise (e.g., Poisson processes) in neural networks, which conflicts with biological observations of correlated activity and deterministic neuron behavior.

Purpose of the Study:

  • To investigate whether spiking neural networks can perform sampling-based Bayesian inference without relying on explicit noise.
  • To analyze the role of neural activity correlations and synaptic plasticity in enabling probabilistic computation in deterministic networks.

Main Methods:

  • Analytical study of auto- and cross-correlations in functional Bayesian spiking networks.
  • Demonstration of how correlations influence synaptic interaction strengths and are controlled by synaptic plasticity.
  • In silico simulations and neuromorphic emulation of interconnected deterministic spiking networks.

Main Results:

  • Neural activity correlations can be translated into controllable synaptic interaction strengths via synaptic plasticity.
  • Small ensembles of deterministic spiking networks can learn to shape their output activity co-dependently.
  • Demonstrated in silico that these networks can perform complex Bayesian computations without explicit noise.

Conclusions:

  • Spiking neural networks do not require explicit noise to perform sampling-based Bayesian inference.
  • Synaptic plasticity and inherent neural correlations provide a mechanism for probabilistic computation in deterministic networks.
  • These findings reduce architectural constraints for biological and artificial neural substrates performing probabilistic computing.