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Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes.

Kohei Ichikawa1,2, Asaki Kataoka1,3

  • 1Graduate School of Arts and Sciences, University of Tokyo, Tokyo 153-0041, Japan.

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Recurrent neural networks (RNNs) utilize dynamical systems for sampling-based probabilistic inference, unlike feedforward neural networks (FFNNs). This mechanism in RNNs enhances estimation accuracy, offering insights into neural information processing.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Animals perform probabilistic inferences using uncertain environmental data.
  • Probabilistic population codes in neural networks (NNs) enable near-optimal point estimation.
  • The mechanism of sampling-based probabilistic inference in NNs remains unclear.

Purpose of the Study:

  • To investigate and compare the sampling mechanisms in feedforward neural networks (FFNNs) and recurrent neural networks (RNNs).
  • To elucidate how NNs perform sampling-based probabilistic inference.

Main Methods:

  • Trained FFNNs and RNNs on sampling-based probabilistic inference tasks.
  • Analyzed and compared the internal sampling mechanisms of both network types.

Main Results:

  • RNNs employ a mechanism leveraging dynamical systems properties for sampling, distinct from FFNNs.
  • Sampling in RNNs acts as an inductive bias, improving estimation accuracy beyond maximum a posteriori methods.
  • Identified differences in information processing strategies between FFNNs and RNNs during probabilistic inference.

Conclusions:

  • RNNs' use of dynamical systems is crucial for efficient sampling-based probabilistic inference.
  • This study provides a mechanistic understanding of sampling in NNs.
  • Findings contribute to understanding the interplay between dynamical systems and neural information processing.