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Goal-Directed Decision Making with Spiking Neurons.

Johannes Friedrich1, Máté Lengyel2

  • 1Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom, and j.friedrich@columbia.edu.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|February 5, 2016
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Summary

This study introduces a spiking neural network model that explains the neural basis of goal-directed decision-making. The model accurately reproduces behavioral and neural data, offering a framework for understanding complex choices.

Keywords:
computational modelingdecision makingneuroeconomicsplanningreinforcement learningspiking neurons

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Decision-making involves habitual and goal-directed action selection.
  • Neural mechanisms for habitual action selection are well-understood.
  • Neural circuit mechanisms for goal-directed decision-making remain largely unknown.

Purpose of the Study:

  • To develop a biologically realistic spiking neural network model for goal-directed decision-making.
  • To explain the online value estimation problem in decision-making.
  • To bridge the gap between computational theory and neural implementation of goal-directed actions.

Main Methods:

  • Developed a spiking neural network model using local plasticity rules.
  • Simulated the model on various decision-making tasks (binary choice, sequential decision-making).
  • Compared model outputs (decision times, choice probabilities, neural activity) with experimental data.

Main Results:

  • The model provably solves the online value estimation problem for goal-directed decision-making.
  • Model performance closely matched behavioral data in decision times and choice probabilities.
  • Simulated neural activity patterns mimicked experimental recordings from frontal cortices.

Conclusions:

  • The study presents the first biologically realistic account of goal-directed decision-making at computational, algorithmic, and implementational levels.
  • The model offers a principled framework for understanding the neural underpinnings of prospective planning in decision-making.
  • Novel predictions are made for sequential decision-making tasks with multiple rewards.