Two roles for choice selective inhibition in decision-making circuits

Abstract

During perceptual decision-making, the firing rates of cortical neurons reflect upcoming choices. Recent work showed that excitatory and inhibitory neurons are equally selective for choice. However, functional consequences of inhibitory choice selectivity in decision-making circuits are unknown. We developed a circuit model of decision-making which accounts for the specificity of inputs to and outputs from inhibitory neurons. We found that selective inhibition expands the space of circuits supporting decision-making, allowing for weaker or stronger recurrent excitation when connected in a competitive or feedback motif. The specificity of inhibitory outputs sets the trade-off between speed and accuracy of decisions by altering the attractor dynamics in the circuit. Recurrent neural networks trained to make decisions display the same dependence on inhibitory specificity and the strength of recurrent excitation. Our results reveal two concurrent roles for selective inhibition in decision-making circuits: stabilizing strongly connected excitatory populations and maximizing competition between oppositely selective populations.

Synaptic wiring motifs in posterior parietal cortex support decision-making

“…In models of decision-making, the formation of categorical choices is often facilitated by non-selective lateral inhibition 14 . Recently, it has been proposed that selective inhibition could play one of two possible roles: promoting competition with anti-selective I-to-E connectivity, or stabilizing dynamics through co-selective I-to-E connectivity 15 . These distinct contributions are also present in the linear rate model presented here (Supp.…”

“…In some cases, inhibitory connectivity is assumed to be non-selective and random, but recent work has shown that inhibitory activity is as selective as excitatory activity, which suggests that inhibitory connectivity may follow choice-selective rules 10 . Indeed, recent models have shown that selective inhibition can crucially alter circuit function by stabilizing network activity or maximizing competition between opposing excitatory pools 15 . However, activity measurements in PPC are consistent with multiple possible circuit architectures 10,15,16 , and the underlying synaptic connectivity has not previously been measured.…”

“…Indeed, recent models have shown that selective inhibition can crucially alter circuit function by stabilizing network activity or maximizing competition between opposing excitatory pools 15 . However, activity measurements in PPC are consistent with multiple possible circuit architectures 10,15,16 , and the underlying synaptic connectivity has not previously been measured.…”

“…Although some decision-making models focus on the production of categorical choices via winner-take-all dynamics in nonlinear attractor models 15,47,48 , previous work suggests that during navigational tasks PPC produces more complex dynamics in which multiple activity patterns arise for each trial type 68 . These neural trajectories in PPC likely represent a wide range of task and behavioral variables, including the mouse's choice, its navigational movements and position, and sensory cues from the environment 4,12,35,[69][70][71][72] .…”

Multiscale and Extended Retrieval of Associative Memory Structures in a Cortical Model of Local-Global Inhibition Balance

“…This may be especially useful when faced with competing behavioral choices and maintaining the distribution of these choices is meaningful, such as in perceptual decision-making ( Najafi et al, 2020 ). Indeed, Roach et al (2022) report that tuned local inhibition can alter the attractor dynamics of perceptual decision-making networks to balance between the speed or accuracy of perceptual decisions.…”