Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback

TL;DR

This paper presents a phenomenological model of the primate visual cortex based on principles of prediction, compression, and feedback, successfully replicating key visual processing features and achieving high classification and tracking performance.

Contribution

It introduces a novel hierarchical cortical model that integrates unsupervised learning, prediction, and contextual feedback to explain visual cortex functions.

Findings

Reproduces key aspects of the primate ventral stream

Achieves state-of-the-art visual tracking on novel objects

Demonstrates effective unsupervised learning through prediction

Abstract

There has been great progress in understanding of anatomical and functional microcircuitry of the primate cortex. However, the fundamental principles of cortical computation - the principles that allow the visual cortex to bind retinal spikes into representations of objects, scenes and scenarios - have so far remained elusive. In an attempt to come closer to understanding the fundamental principles of cortical computation, here we present a functional, phenomenological model of the primate visual cortex. The core part of the model describes four hierarchical cortical areas with feedforward, lateral, and recurrent connections. The three main principles implemented in the model are information compression, unsupervised learning by prediction, and use of lateral and top-down context. We show that the model reproduces key aspects of the primate ventral stream of visual processing including Simple and Complex cells in V1, increasingly complicated feature encoding, and increased separability of object representations in higher cortical areas. The model learns representations of the visual environment that allow for accurate classification and state-of-the-art visual tracking performance on novel objects.