Spatially distributed computation in cortical circuits

TL;DR

This paper proposes a neural wave interference framework for cortical computation, challenging the traditional view of specialized neurons by demonstrating distributed responses and contextual interactions through modeling, psychophysics, and electrophysiology.

Contribution

It introduces neural wave interference as an alternative to the standard neural computation model, emphasizing distributed responses and stimulus interactions in cortical circuits.

Findings

Neural responses are distributed among neurons, forming characteristic waveforms.

Stimulus responses arise from interference of neural waves in cortical circuits.

The neural wave interference model aligns with biological vision responses.

Abstract

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron's response to one dimension strongly depends on other dimensions. Here we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Resulting patterns of interference depend on interaction between stimulus dimensions. Comparison of these modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation.