The Inter-Regional Interactions Underlying the Decision to Act, at Spiking Resolution

The adaptive value of cognition manifests not only in the arbitration of how to act but also of when. Though much neuroscientific investigation of decision-making has focused on sensory-guided perceptual decisions, somewhat distinct neural processes appear to underlie internally generated (self-driven) decisions of when to act. Since early electroencephalographic recordings in humans identified a ‘readiness potential’ that precedes the initiation of self-driven movements, numerous cortical and subcortical regions have been functionally implicated in the self-driven decision to act across different mammalian models. However, the historical challenge of assessing interactions between these regions at the temporal resolution of spiking has kept functional ideas poorly tested and mechanistic understanding out of reach. Our collaboration aims to unify the disparate literature on regions functionally implicated in self-driven action decisions and clarify relevant neural activity dynamics across these regions. Our approach will involve a range of emerging experimental and computational methods to develop network models that explain the decision to act through interactions between neuronal populations on the timescale of synaptic communication. We will use a climbing paradigm for head-fixed mice that we have recently developed, leveraging the self-driven decision to initiate climbing that naturally emerges in this paradigm. We will employ a comparative task design that pairs self-driven, cue-based, and reward-contingent action decisions, allowing us to distinguish neural activity underlying action decision, initiation, and movement planning. Simultaneous multi-array, multi-region recording across implicated regions will allow us to quantify interactions between neuronal populations with cellular and spike resolution. Characterization of neural activity dynamics will help unify models, clarify the flow of decision-related activity, and expose relevant network architectures. We will pair contemporary approaches for online detection of population activity events and optogenetics to causally test and iteratively refine resulting network models. We expect our work to radically improve mechanistic understanding of self-driven action decisions, a fundamental and long-studied aspect of cognition.