Learning to infer in recurrent biological networks

Abstract

A popular theory of perceptual processing holds that the brain learns both a generative model of the world and a paired recognition model using variational Bayesian inference. Most hypotheses of how the brain might learn these models assume that neurons in a population are conditionally independent given their common inputs. This simplification is likely not compatible with the type of local recurrence observed in the brain. Seeking an alternative that is compatible with complex inter-dependencies yet consistent with known biology, we argue here that the cortex may learn with an adversarial algorithm. Many observable symptoms of this approach would resemble known neural phenomena, including wake/sleep cycles and oscillations that vary in magnitude with surprise, and we describe how further predictions could be tested. We illustrate the idea on recurrent neural networks trained to model image and video datasets. This framework for learning brings variational inference closer to neuroscience and yields multiple testable hypotheses.

References in corpus (14)

• Auto-Encoding Variational Bayes
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• Unsupervised Learning of Video Representations using LSTMs
• Adversarial Feature Learning
• Adversarially Learned Inference
• VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
• Large Scale Adversarial Representation Learning
• Stochastic Adversarial Video Prediction
• How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation
• Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
• Inference by Reparameterization in Neural Population Codes
• Hierarchical Adversarially Learned Inference