Analysis of visual processing capabilities and neural coding strategies of a detailed model for laminar cortical microcircuits in mouse V1

Abstract

Neural networks of the brain that process visual information have structural properties that differ significantly from those of neural networks which are commonly used for visual processing in AI, such as Convolutional Neural Networks (CNNs). But it has remained unknown how these structural differences are related to network function. We analyze visual processing capabilities of a large-scale model for area V1 that arguably provides the most comprehensive accumulation of anatomical and neurophysiological data that is currently available. Its network structure turns out to induce a number of characteristic visual processing capabilities of the brain, in particular the capability to multiplex different visual processing tasks, also on temporally dispersed visual information, with remarkable robustness to noise. This V1 model also exhibits a number of characteristic neural coding properties of the brain, which provide explanations for its superior noise robustness. Since visual processing in the brain is substantially more energy-efficient than implementations of CNNs in common computer hardware, such brain-like neural network models are likely to have also an impact on technology: As blueprints for visual processing in more energy-efficient neuromorphic hardware. TeaserA new generation of neural network models based on neurophysiological data can achieve robust multiplexing capabilities.