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Combining Sampling Methods with Attractor Dynamics in Spiking Models of Head-Direction Systems.

Vojko Pjanovic1,2, Jacob Zavatone-Veth3, Paul Masset4

  • 1Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.

Biorxiv : the Preprint Server for Biology
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Summary
This summary is machine-generated.

This study introduces a novel spiking neural network model for the head-direction system, integrating sampling-based inference with attractor dynamics to navigate uncertainty and guide behavior.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Neural Dynamics

Background:

  • The brain must infer and integrate noisy environmental signals to guide behavior effectively, a process complicated by inherent uncertainty.
  • Sampling-based inference is a proposed mechanism for handling uncertainty, particularly in early sensory processing.
  • Reconciling sampling-based methods with higher-order brain area dynamics, like attractor dynamics, remains a challenge.

Purpose of the Study:

  • To present a spiking neural network model for the head-direction (HD) system that unifies sampling-based inference with attractor dynamics.
  • To derive the necessary spiking neural network dynamics for sampling from diverse probability distributions, including those with Poisson noise.
  • To propose a method for updating head direction estimates by integrating angular velocity samples with attractor dynamics.

Main Methods:

  • Developed a spiking neural network model for the head-direction system.
  • Derived spiking neural network dynamics to perform sampling from probability distributions with Poisson noise.
  • Integrated angular velocity samples with a circular manifold to maintain attractor dynamics for head direction estimation.

Main Results:

  • The model successfully combines sampling-based inference with attractor dynamics in the HD system.
  • It generates specific, testable predictions for neurophysiological experiments, including correlated voltage fluctuations and firing patterns.
  • Predicted characteristic statistics for the movement of the neural activity 'bump' representing head direction.

Conclusions:

  • The study extends theories of probabilistic sampling with spiking neurons.
  • It offers a new perspective on neural computations for orientation and navigation.
  • Supports the integration of sampling-based methods and attractor dynamics as a framework for neural dynamics research.