Choice selective inhibition drives stability and competition in decision circuits

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, the 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 either stabilizing or destabilizing the saddle-point dynamics underlying decisions 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. In decision circuits, inhibitory neurons signal animal choices. Here, the authors show that choice-selective inhibition can stabilize the circuit dynamics or promote competition depending on inhibitory output connections, affecting choice behavior.

Automated summary

Recent research found that both excitatory and inhibitory neurons are equally selective for choice during perceptual decision-making. However, the functional consequences of inhibitory choice selectivity in decision-making circuits remained unclear. By developing a circuit model, the authors demonstrated that selective inhibition expands the range of decision-making circuits, enabling weaker or stronger recurrent excitation in competitive or feedback motifs. The specificity of inhibitory outputs determines the trade-off between decision speed and accuracy by stabilizing or destabilizing the underlying dynamics in the circuit. Their findings highlight the dual roles of selective inhibition in decision-making circuits: stabilizing strongly connected excitatory populations and maximizing competition between opposite selective populations. Inhibitory neurons in decision circuits convey animal choices.