Computation timescales as an organizing principle of large-scale cortical dynamics during decision making

The ability to make decisions lies at the core of cognition, but much remains unknown about its underlying neural mechanisms. Part of what has hindered progress is the fact that decisions vary widely in terms of the underlying computations (e.g. stimulus processing, memory) and their timescales (split seconds, minutes). Until recently, another hurdle had been our limited ability to record neural dynamics at large scales. Thus, most of what we know about decision making has come from studies focusing on the role of single regions during single tasks. Yet, it has become increasingly clear that decision making is supported by distributed cortical dynamics. However, we still do not understand how the features of distributed dynamics vary as a function of the types and timescales of cognitive computations underlying particular decisions. We aim to fill this gap by recording and perturbing neural dynamics across the cortex with mesoscale or cellular resolution, over a parameterized task space of decision-making computations and timescales thereof. Our hypothesis is that a key task feature organizing large-scale cortical dynamics is the timescale over which its underlying computations occur. The cortex is known to be organized in a hierarchy of intrinsic timescales, whereby frontal areas have longer autocorrelation functions than posterior sensory areas. However, we do not know if this hierarchy can explain why and how cortical areas are recruited in a task-dependent fashion. Moreover, we do not understand which local circuit features are causal to area-level timescales, or how they are modulated by task demands. To answer these questions, we are developing a novel task for head-fixed mice making navigational decisions in virtual reality. This is a modified version of a delayed match-to-sample task, where the sample is noisy and must therefore be gradually accrued. We independently vary the timescales of accrual and sample memory, and accrual difficulty, to generate a thr