Learning probability distributions of sensory inputs with Monte Carlo predictive coding

Gaspard Oliviers, Rafal Bogacz, Alexander Meulemans

Abstract: 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.