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This study introduces the iterative Poisson variational autoencoder (iP-VAE), a novel recurrent spiking neural network model that unifies brain and machine inference. The iP-VAE demonstrates superior performance in reconstruction and generalization, offering a biologically plausible approach to artificial intelligence.

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Artificial Intelligence

Background:

  • Inference in biological brains and artificial systems can be unified by optimizing shared objectives like the evidence lower bound (ELBO) or variational free energy (F).
  • The precise neural mechanisms for implementing variational inference remain an open question in neuroscience.
  • Existing models often lack biological plausibility or struggle with generalization.

Purpose of the Study:

  • To demonstrate that online natural gradient descent on variational free energy (F) can yield a recurrent spiking neural network architecture.
  • To introduce the iterative Poisson variational autoencoder (iP-VAE) as a biologically plausible model for variational inference.
  • To evaluate the empirical performance and biological plausibility of the proposed iP-VAE model.

Main Methods:

  • Derived a recurrent spiking neural network model from first principles using online natural gradient descent on variational free energy under Poisson assumptions.
  • Developed the iterative Poisson variational autoencoder (iP-VAE) by replacing the standard encoder with local updates from natural gradient descent.
  • Empirically evaluated the iP-VAE against standard VAEs and Gaussian predictive coding models on tasks involving sparsity, reconstruction, and generalization.

Main Results:

  • The iP-VAE model performs variational inference through emergent membrane potential dynamics.
  • The model exhibits emergent normalization via lateral competition and utilizes hardware-efficient integer spike count representations.
  • iP-VAE outperformed standard VAEs and Gaussian predictive coding models in sparsity, reconstruction, and biological plausibility.
  • iP-VAE demonstrated superior generalization to out-of-distribution inputs compared to hybrid iterative-amortized VAEs.

Conclusions:

  • Deriving inference algorithms from first principles can lead to concrete neural network architectures that are both biologically plausible and empirically effective.
  • The iP-VAE offers a promising unified framework for understanding inference in brains and machines.
  • This work bridges the gap between theoretical machine learning objectives and practical neural implementations.