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Evidence Accumulation and Change Rate Inference in Dynamic Environments.

Adrian E Radillo1, Alan Veliz-Cuba2, Krešimir Josić3

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Animals adapt to changing environments by learning the rate of environmental change. This study presents a model for optimal decision-making, showing how neural networks can perform these complex environmental inferences.

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Area of Science:

  • Computational neuroscience
  • Animal behavior
  • Decision-making under uncertainty

Background:

  • Animals must adapt decisions to environmental volatility.
  • Accurate discounting of outdated information requires learning environmental change rates.
  • Inferring environmental change is crucial for survival and optimal decision-making.

Purpose of the Study:

  • To develop an ideal observer model for inferring environmental state and change rate.
  • To simplify complex computations using a moment closure approximation in the continuum limit.
  • To map optimal inference computations to plausible neural mechanisms.

Main Methods:

  • Developed an ideal observer model for state and change rate inference.
  • Utilized a moment closure approximation to simplify computations in the continuum limit.
  • Modeled approximate computations using a neural network with a rate-correlation-based plasticity rule.

Main Results:

  • The simplified model accurately infers environmental state and change rate, comparable to the ideal observer.
  • The low-dimensional system effectively handles the computational challenges of growing possibilities.
  • Neural network models can perform these approximate computations via specific plasticity rules.

Conclusions:

  • Optimal observers accumulate evidence efficiently in changing environments.
  • This work provides a bridge between ideal observer computations and plausible neural mechanisms for inference.
  • The findings offer insights into how biological systems adapt to dynamic environments.