Cognition is produced by the continuous interactions between many regions across the brain, but has typically been studied one brain region at a time. How signals in different regions coordinate to achieve a single coherent action remains unclear. Here, we address this question by characterizing the simultaneous interactions between up to 20 brain regions across the brain (10 targeted regions per hemisphere), of rats performing the “Poisson Clicks” task, a decision-making task that demands the gradual accumulation of momentary evidence. Using 8 Neuropixels probes in each animal, we recorded simultaneously in prefrontal cortex, striatum, motor cortex, hippocampus, amygdala, and thalamus. To assess decision-related interactions between regions, we quantified correlations of each region’s “decision variable”: moment-to-moment co-fluctuations along the axis in neural state space that best predicts the upcoming choice. This revealed a network of strongly correlated brain regions that include the dorsomedial frontal cortex (dmFC), anterior dorsal striatum (ADS), and primary motor cortex (M1), whose decision variables also led the rest of the brain. If coordinated activity within this subnetwork reflects an ongoing evidence accumulation process, these correlations should cease at the time of decision commitment. We therefore compared correlations before versus after “nTc”, a recently reported estimator for the time of internal decision commitment. We found that correlations in the decision variables between different brain regions decayed to near-zero after nTc. Additionally, we found that choice-predictive activity steadily increased over time before nTc, but abruptly stopped growing at nTc, consistent with an evidence accumulation process that has stopped evolving at that time. Assessing nTc from the activity of individual regions revealed that nTc could be reliably detected earlier in M1 than other regions. These results show that evidence accumulation involves coordination within a network of frontal cortical and striatal regions, and suggests that termination of this process may initiate in M1.

A common approach in the study of cognition is to train subjects to perform a task that requires a particular cognitive process to solve. Analysis of the subjects’ response behavior while they perform these tasks can offer valuable insight into the underlying mechanisms that give rise to cognition. However, if subjects are able to accurately perform such a task by using a strategy that doesn’t involve the targeted cognitive process, data from those experiments becomes more difficult to interpret. A number of perceptual decision-making tasks have been designed to study the accumulation of evidence, i.e. how noisy information presented over time is used to form a decision. Recent work, however, has highlighted how a variety of non-integration strategies can by some measures yield strikingly near-optimal performance on such tasks, raising the possibility that past conclusions from these experiments may be incorrect. Here we assemble the largest data set of animals performing one such task – the “Poisson Clicks” task – which is optimally solved by the gradual integration of pulsatile auditory noise. To investigate whether rats are in fact using this strategy, we compiled data from 515 rats performing over 35 million trials. We compare performance of 3 degenerate strategies (that circumvent the need to integrate evidence) to the optimal (integration) strategy. We demonstrate that the pulsatile nature of the stimuli used in the Poisson Clicks Task makes it possible to distinguish which strategy subjects use. Overwhelmingly, we find the rats are using an integration strategy when performing the Poisson Clicks Task.

Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks. However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We found that trajectories evolved along two sequential regimes, the first dominated by sensory inputs, and the second dominated by the autonomous dynamics, with flow in a direction (i.e., “neural mode”) largely orthogonal to that in the first regime. We propose that the second regime corresponds to decision commitment. We developed a simplified model that approximates the coupled transition in dynamics and neural mode and allows precise inference, from each trial’s neural activity, of a putative internal decision commitment time in that trial. The simplified model captures diverse and complex single-neuron temporal profiles, such as ramping and stepping. It also captures trial-averaged curved trajectories, and reveals distinctions between brain regions. The putative neurally-inferred commitment times (“nTc”) occurred at times broadly distributed across trials, and not time-locked to stimulus onset, offset, or response onset. Nevertheless, when trials were aligned to nTc, behavioral analysis showed that, as predicted by a decision commitment time, sensory evidence before nTc affected the subjects’ decision, but evidence after nTc did not. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process, and suggest the moment of commitment to be a useful entry point for dissecting mechanisms underlying rapid changes in internal state.

The use of Neuropixels probes for chronic neural recordings is in its infancy and initial studies leave questions about long-term stability and probe reusability unaddressed. Here, we demonstrate a new approach for chronic Neuropixels recordings over a period of months in freely moving rats. Our approach allows multiple probes per rat and multiple cycles of probe reuse. We found that hundreds of units could be recorded for multiple months, but that yields depended systematically on anatomical position. Explanted probes displayed a small increase in noise compared to unimplanted probes, but this was insufficient to impair future single-unit recordings. We conclude that cost-effective, multi-region, and multi-probe Neuropixels recordings can be carried out with high yields over multiple months in rats or other similarly sized animals. Our methods and observations may facilitate the standardization of chronic recording from Neuropixels probes in freely moving animals.

Attention is a common but highly complex term associated with a large number of distinct behavioral and perceptual phenomena. In the brain, attention-related changes in neuronal activity are observed in widespread structures. The many distinct behavioral and neuronal phenomena related to attention suggest that it might be subdivided into components corresponding to distinct biological mechanisms. Recent neurophysiological studies in monkeys have isolated behavioral changes related to attention along the 2 indices of signal detection theory and found that these 2 behavioral changes are associated with distinct neuronal changes in different brain areas. These results support the view that attention is made up of distinct neurobiological mechanisms.

Visual attention is associated with neuronal changes across the brain, and these widespread signals are generally assumed to underlie a unitary mechanism of attention. However, using signal detection theory, attention-related effects on performance can be partitioned into changes in either the subject's criterion or sensitivity. Neuronal modulations associated with only sensitivity changes were previously observed in visual cortex, raising questions about which structures mediate attention-related changes in criterion and whether individual neurons are involved in multiple components of attention. Here, we recorded from monkey lateral prefrontal cortex (LPFC) and found that, in contrast to visual cortex, neurons in LPFC changed their firing rates, pairwise correlation, and Fano factor when subjects changed either their criterion or their sensitivity. These results indicate that attention-related neuronal modulations in separate brain regions are not a monolithic signal and instead can be linked to distinct behavioral changes.

Neuronal signals related to visual attention are found in widespread brain regions, and these signals are generally assumed to participate in a common mechanism of attention. However, the behavioral effects of attention in detection can be separated into two distinct components: spatially selective shifts in either the criterion or sensitivity of the subject. Here we show that a paradigm used by many single-neuron studies of attention conflates behavioral changes in the subject's criterion and sensitivity. Then, using a task designed to dissociate these two components, we found that multiple aspects of attention-related neuronal modulations in area V4 of monkey visual cortex corresponded to behavioral shifts in sensitivity, but not criterion. This result suggests that separate components of attention are associated with signals in different brain regions and that attention is not a unitary process in the brain, but instead consists of distinct neurobiological mechanisms.