# Feedforward extraction of behaviorally significant information by neocortical columns

## TL;DR

The paper explains how neocortical columns extract meaningful information from sensory inputs using a modular nonlinear process involving different layers of neurons.

## Contribution

The paper introduces a model of cortical columns that captures how contextually predictable features are extracted and represented in a way consistent with known cortical organization.

## Key findings

- The model reveals a limited number of contextually predictable orthogonal basis features in the receptive field of the cortical column.
- Upper-layer cells generate an overcomplete Hadamard-like representation of basis features, enabling higher-order categorization.
- The model shows that 80% of upper-layer cells acquire complex-cell properties, matching real V1 cortex.

## Abstract

Neurons throughout the neocortex exhibit selective sensitivity to particular features of sensory input patterns. According to the prevailing views, cortical strategy is to choose features that exhibit predictable relationship to their spatial and/or temporal context. Such contextually predictable features likely make explicit the causal factors operating in the environment and thus they are likely to have perceptual/behavioral utility. The known details of functional architecture of cortical columns suggest that cortical extraction of such features is a modular nonlinear operation, in which the input layer, layer 4, performs initial nonlinear input transform generating proto-features, followed by their linear integration into output features by the basal dendrites of pyramidal cells in the upper layers. Tuning of pyramidal cells to contextually predictable features is guided by the contextual inputs their apical dendrites receive from other cortical columns via long-range horizontal or feedback connections. Our implementation of this strategy in a model of prototypical V1 cortical column, trained on natural images, reveals the presence of a limited number of contextually predictable orthogonal basis features in the image patterns appearing in the column’s receptive field. Upper-layer cells generate an overcomplete Hadamard-like representation of these basis features: i.e., each cell carries information about all basis features, but with each basis feature contributing either positively or negatively in the pattern unique to that cell. In tuning selectively to contextually predictable features, upper layers perform selective filtering of the information they receive from layer 4, emphasizing information about orderly aspects of the sensed environment and downplaying local, likely to be insignificant or distracting, information. Altogether, the upper-layer output preserves fine discrimination capabilities while acquiring novel higher-order categorization abilities to cluster together input patterns that are different but, in some way, environmentally related. We find that to be fully effective, our feature tuning operation requires collective participation of cells across 7 minicolumns, together making up a functionally defined 150 μm diameter “mesocolumn.” Similarly to real V1 cortex, 80% of model upper-layer cells acquire complex-cell receptive field properties while 20% acquire simple-cell properties. Overall, the design of the model and its emergent properties are fully consistent with the known properties of cortical organization. Thus, in conclusion, our feature-extracting circuit might capture the core operation performed by cortical columns in their feedforward extraction of perceptually and behaviorally significant information.