A Brain-Wide Map of Neural Activity during Complex Behaviour

Abstract

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with prior expectations to initiate movements. It has proven difficult to meet this challenge when different laboratories apply different analyses to different recordings in different regions during different behaviours. Here, we report a comprehensive set of recordings from 115 mice in 11 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 547 Neuropixels probe insertions covering 267 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity en- codes key task variables. Representations of visual stimuli appeared transiently in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of mid- and hindbrain regions that also encoded choices. Neural responses correlated with motor action almost everywhere in the brain. Responses to reward delivery and consumption versus reward omission were also widespread. Representations of objective prior expectations were weaker, found in sparse sets of neurons from restricted regions. This publicly available dataset represents an unprecedented resource for understanding how computations distributed across and within brain areas drive behaviour.

The flow of reward information through neuronal ensembles in the accumbens

“…40,47–49 They conclude it might partly or primarily reflect the licking movements required for reward consumption rather than the hedonic aspects of reward. 37 Here, we confirmed the important role of licking, but thanks to our task design, we could also clearly distinguish reward signals from those encoding licking itself.…”

“…27 Likewise, our correlation matrix leverages data from multiple sessions and rats, capturing the strength of “functional” connections related to task events in a kind of “meta-brain” of 17 animals performing the same behavioral task, and thus, it lacks individual-level detail because of restrictions imposed by current recording methods. Notably, even recent advancements like Neuropixels are primarily optimized for head-fixed setups, 37,47,49 but see 71 . We need novel high-density electrode technologies for freely moving rodents to record more neurons simultaneously within a single brain, unlocking a deeper understanding of individual neural dynamics under more naturalistic conditions than those provided by head-fixed setups.…”

Sequential and dynamic coding of water-sucrose categorization in rat gustatory cortices

“…In both gustatory cortices, the Stimulus was the less represented variable, with 11.6 and 14.22% of the Stimulus-encoding neurons in the aIC and the OFC, respectively (z =-1.109, p = 0.267); followed by the Choice-encoding subset, with 30.15 and 34.36% cells (z =-1.278, p = 0.200); and finally, the Outcome-encoding subset with 54.12 and 54.74% of neurons in aIC and OFC, respectively (z =-1.175, p = 0.857). Thus, a single drop of taste stimulus triggered a brain-wave activity that gradually recruited more and more cortical neurons, from Stimulus delivery up to the reward Outcome, encompassing all relevant task-variables that may give rise to trial structure representation (Fonseca et al, 2018; Laboratory et al, 2023). Furthermore, we found relatively small neuronal sub-populations that encoded more than one variable ( Figure 2 Supplementary 1 ).…”

Oscillations in an Artificial Neural Network Convert Competing Inputs into a Temporal Code

“…Alternatively, these predictions could be tested based on intracranial recordings from the mouse brain. Using Neuropixels probes, spiking activity and local field potentials can be simultaneously recorded intracranially from several cortical areas in mice (and non-human primates, [53, 107]. It is well-established that the mouse visual system exhibits a hierarchical structure similar to the one observed in primates [116, 102].…”