Learning probability distributions of sensory inputs with Monte Carlo predictive coding

It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis has been formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, classic predictive coding theory proposed how the probabilistic models can be learned by networks of neurons employing local synaptic plasticity. On the other hand, neural sampling theories have demonstrated how stochastic dynamics enable neural circuits to represent the posterior distributions of latent states of the environment. These frameworks were brought together by variational filtering that introduced neural sampling to predictive coding. Here, we consider a variant of variational filtering for static inputs, to which we refer as Monte Carlo predictive coding (MCPC). We demonstrate that the integration of predictive coding with neural sampling results in a neural network that learns precise generative models using local computation and plasticity. The neural dynamics of MCPC infer the posterior distributions of the latent states in the presence of sensory inputs, and can generate likely inputs in their absence. Furthermore, MCPC captures the experimental observations on the variability of neural activity during perceptual tasks. By combining predictive coding and neural sampling, MCPC can account for both sets of neural data that previously had been explained by these individual frameworks.Author summary: Understanding how the brain interprets its sensory information is fundamental to neuroscience. It is suggested that the brain processes information by updating models of the environment that exist inside the brain. These models make educated guesses about the world, relying on the noisy information received through our senses. However, translating this conceptual framework into a concrete, biological theory is challenging. Several proposed theories explain specific aspects of brain function or dynamics. For instance, predictive coding describes the organization of the brain which is important for understanding how the brain infers and learns. Other theories, such as neural sampling, use random changes in the brain’s activity to explain how the brain interprets its sensory inputs. However, these theories remain separate, each explaining only certain brain functions. Our research introduces a theory that combines predictive coding and neural sampling into a unified framework for understanding brain learning and information processing. This model mirrors the brain’s organization, information processing capabilities using local computations, and learning using local plasticity. It also accounts for experimentally observed characteristics of the brain’s activity, while relying on minimal assumptions. Overall, our model offers a more comprehensive understanding of the brain’s learning capabilities, relevant to both neuroscience and machine learning.