A unified circuit model of attention: Neural and behavioral effects

Abstract

Selective visual attention modulates neural activity in the visual system and leads to enhanced performance on difficult visual tasks. Here, we use an existing circuit model of visual cortex, known as the stabilized supralinear network, to demonstrate that many neural correlates of attention can arise from simple circuit mechanisms. Using different variants of the model we replicate results from studies of both feature and spatial attention. In addition to firing rate changes, we also replicate findings regarding how attention impacts trial-to-trial variability. Finally, we expand this circuit model into an architecture that can perform visual tasks in order to show that these neural effects can enhance detection performance. This work advances our understanding of the physical underpinnings of attention.

VIP interneurons in mouse primary visual cortex selectively enhance responses to weak but specific stimuli

“…Pyr → VIP → SST → Pyr) that depends upon, and contributes to, network dynamics. Stabilized supralinear network (SSN) models have been proposed to account for a variety of contrast-dependent response properties in visual cortex ( Rubin et al, 2015 ; Ahmadian et al, 2013 ), including the transition from a high gain regime at low contrast to a feedback inhibition dominated low gain regime at high contrast ( Adesnik, 2017 ; Sanzeni et al, 2020 ), as well as cortical noise correlations ( Hennequin et al, 2018 ), surround suppression ( Liu et al, 2018 ), and effects of feature and spatial attention on neural activity ( Lindsay et al, 2020 ). In SSNs, high gain is achieved through supralinear single-neuron transfer functions (e.g.…”

Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

“…The long history of modeling the circuitry of visual cortex can provide more ideas about what details to incorporate and how. A pre-existing circuit model of V1 anatomy and function [107], for example, was placed into the architecture of a CNN and used to replicate effects of visual attention [108]. A more extreme approach to adding biological detail can be found in [109], where the connectome of the fly visual system defined the architecture of the model, which was then trained to perform visual tasks.…”

A spatiotemporal mechanism of visual attention: Superdiffusive motion and theta oscillations of neural population activity patterns

“…In addition, experimental evidence supporting the presence of inhibition of return both at the behavioral and neural levels remains controversial (19,20). On the other hand, neural circuit models for explaining the neural effects of top-down attention often treat them as static inhibitory (21) or excitatory inputs (22,23) to local circuits; doing so, thus, completely ignores the dynamical fluctuations of attention. Therefore, despite widespread investigations, the fundamental questions of the neural circuit mechanism underlying attention fluctuations and their functional role remain unclear.…”