Ensemble leaa-ning by vm-iational free energy minimization is a framework for statistical inference in which aa ensemble of paa-ameter vectors is optimized rather thaa a single paa-ameter vector. The ensemble approximates the posterior probability distribution of the paa-ameters.

This paper summarizes our recent attempts to integrate action and perception within a single optimization framework. We start with a statistical formulation of Helmholtz’s ideas about neural energy to furnish a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring the causes of our sensory inputs and learning regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and contextsensitive fashion. This scheme provides a principled way to understand many aspects of the brain’s organization and responses. We will demonstrate the brain-like dynamics that this scheme entails by using models of bird songs that are based on chaotic attractors with autonomous dynamics. This provides a nice example of how nonlinear dynamics can be exploited by the brain to represent and predict dynamics in the environment.

In the classical feed-forward, modular view of visual processing, the primary visual cortex (area V1) is a module that serves to extract local features such as edges and bars. Representation and recognition of objects are thought to be functions of higher extrastriate cortical areas. This paper presents neurophysiological data that show the later part of V1 neurons ’ responses reflecting higher order perceptual computations related to Ullman’s (Cognition 1984;18:97–159) visual routines and Marr’s (Vision NJ: Freeman 1982) full primal sketch, 2 1 2D sketch and 3D model. Based on theoretical reasoning and the experimental evidence, we propose a possible reinterpretation of the functional role of V1. In this framework, because of V1 neurons ’ precise encoding of orientation and spatial information, higher level perceptual computations and representations that involve high resolution details, fine geometry and spatial precision would necessarily involve V1 and be reflected in the later part of its neurons ’ activities. © 1998

Suppression of synaptic transmission may allow combination of associative feedback and self-organizing feedforward connections in the neocortex