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Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.

Javier A Caballero1, Nathan F Lepora2, Kevin N Gurney3

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This study introduces a novel computational model for brain decision-making using neural spike intervals. The new model links neural signaling to behavior, explaining decision time laws.

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Area of Science:

  • Computational Neuroscience
  • Decision Neuroscience
  • Neural Coding

Background:

  • Current decision-making models often abstract away from neural signaling.
  • A gap exists between continuous evidence accumulation and discrete neural spikes.

Purpose of the Study:

  • To develop a decision-making model based on neural inter-spike intervals (ISIs).
  • To link neural signaling properties to behavioral decision-making phenomena.

Main Methods:

  • Introduced a variant of the multi-hypothesis sequential probability ratio test (MSPRT) using ISIs.
  • Developed a new likelihood function for spike-based evidence.
  • Analyzed the model's performance with realistic ISI distributions.

Main Results:

  • The proposed s-MSPRT model links neural signaling to behavioral observations.
  • Demonstrated that the refractory period can shorten decision times.
  • Showed performance relates to Kullback-Leibler divergence (KLD) between ISI distributions.

Conclusions:

  • The s-MSPRT model provides a neural basis for decision-making theories.
  • The model explains Hick's law and Piéron's law through information gain.
  • Spike train analysis can predict behavior in multi-alternative choice tasks.