Impact of precisely-timed inhibition of gustatory cortex on taste behavior depends on single-trial ensemble dynamics

Sensation and action are necessarily coupled during stimulus perception – while tasting, for instance, perception happens while an animal decides to expel or swallow the substance in the mouth (the former via a behavior known as ‘gaping’). Taste responses in the rodent gustatory cortex (GC) span this sensorimotor divide, progressing through firing-rate epochs that culminate in the emergence of action-related firing. Population analyses reveal this emergence to be a sudden, coherent and variably-timed ensemble transition that reliably precedes gaping onset by 0.2–0.3s. Here, we tested whether this transition drives gaping, by delivering 0.5s GC perturbations in tasting trials. Perturbations significantly delayed gaping, but only when they preceded the action-related transition - thus, the same perturbation impacted behavior or not, depending on the transition latency in that particular trial. Our results suggest a distributed attractor network model of taste processing, and a dynamical role for cortex in driving motor behavior.

Our study follows up on more than 10 publications (from several research groups) about single neuron coding of tastes in primary sensory cortex. As such, we first replicate the (already published) basic features of the single neuron cortical taste code ( Figure 3) with a sample of neurons (>300 single units) that is at least twice as large as any of the previous studies. In the rest of our study, we investigate novel effects that have never been observed and in addition, do so through "within-session" analyses (that is, only a subset of trials in a session was used in each analysis). While within-session analyses have the potential of winnowing down the effective number of trials being analyzed, they also ensure that we (purposefully) focus only on the most substantial effects produced by our manipulation. Details about replication/technical robustness are in the Materials and Methods section. We applied Bayesian statistical techniques in our analyses (specific models are detailed in the Materials and Methods section) that are robust to outliers and avoided excluding data from our analyses.

Statistical reporting
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Group allocation
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Our study involves using within-subject controls, such that every animal in the study experiences all the conditions of the experiment. Details on the experimental set up and study design are provided in the Materials and Methods section.
Being dense recordings of large ensembles of neurons, our data files are usually very large in size (running into tens of GBs). We have, therefore, made our data accessible on a public fileshare system maintained by Library and Technology Services (LTS) in Brandeis University. We can give any interested researchers access to the datasets through Brandeis LTS, but the files are prohibitively large to be hosted on a more general file-share platform.