Controlled hierarchical filtering: Model of neocortical sensory processing

TL;DR

This paper presents a hierarchical filtering model of neocortical sensory processing emphasizing predictive learning of internal models, linking control architectures to hippocampal structures, and demonstrating stability and near-optimal policy convergence.

Contribution

It introduces a novel generative control architecture mapped to neocortex and hippocampus, with mathematical guarantees and connections to reinforcement learning.

Findings

Model predicts implicit memory phenomena like priming.

Mathematical theorems ensure stability and learning properties.

Control network converges to near-optimal policies.

Abstract

A model of sensory information processing is presented. The model assumes that learning of internal (hidden) generative models, which can predict the future and evaluate the precision of that prediction, is of central importance for information extraction. Furthermore, the model makes a bridge to goal-oriented systems and builds upon the structural similarity between the architecture of a robust controller and that of the hippocampal entorhinal loop. This generative control architecture is mapped to the neocortex and to the hippocampal entorhinal loop. Implicit memory phenomena; priming and prototype learning are emerging features of the model. Mathematical theorems ensure stability and attractive learning properties of the architecture. Connections to reinforcement learning are also established: both the control network, and the network with a hidden model converge to (near) optimal policy under suitable conditions. Falsifying predictions, including the role of the feedback connections between neocortical areas are made.