Brendan Ito (Cornell University, USA) and Teja Bollu (Salk Institute, USA) share unique insights into rapid online motor corrections during mouse licking, analogous to primate goal-oriented reaching. Techniques covered include large-scale single unit recording during behaviour with optogenetics, and a deep-learning-based neural network to resolve 3D tongue kinematics during licking.

Human movement disorders and pharmacological studies have long suggested molecular dopamine modulates the pace of the internal clock. But how does the endogenous dopaminergic system influence the timing of our movements? We examined the relationship between dopaminergic signaling and the timing of reward-related, self-timed movements in mice. Animals were trained to initiate licking after a self-timed interval following a start cue; reward was delivered if the animal’s first lick fell within a rewarded window (3.3-7 s). The first-lick timing distributions exhibited the scalar property, and we leveraged the considerable variability in these distributions to determine how the activity of the dopaminergic system related to the animals’ timing. Surprisingly, dopaminergic signals ramped-up over seconds between the start-timing cue and the self-timed movement, with variable dynamics that predicted the movement/reward time, even on single trials. Steeply rising signals preceded early initiation, whereas slowly rising signals preceded later initiation. Higher baseline signals also predicted earlier self-timed movement. Optogenetic activation of dopamine neurons during self-timing did not trigger immediate movements, but rather caused systematic early-shifting of the timing distribution, whereas inhibition caused late-shifting, as if dopaminergic manipulation modulated the moment-to-moment probability of unleashing the planned movement. Consistent with this view, the dynamics of the endogenous dopaminergic signals quantitatively predicted the moment-by-moment probability of movement initiation. We conclude that ramping dopaminergic signals, potentially encoding dynamic reward expectation, probabilistically modulate the moment-by-moment decision of when to move. (Based on work from Hamilos et al., eLife, 2021).

Sequential temporal ordering and patterning are key features of natural signals, used by the brain to decode stimuli and perceive them as sensory objects. Touch is one sensory modality where temporal patterning carries key information, and the rodent whisker system is a prominent model for understanding neuronal coding and plasticity underlying touch sensation. Neurons in this system are precise encoders of fluctuations in whisker dynamics down to a timescale of milliseconds, but it is not clear whether they can refine their encoding abilities as a result of learning patterned stimuli. For example, can they enhance temporal integration to become better at distinguishing sequences? To explore how cortical coding plasticity underpins sequence discrimination, we developed a task in which mice distinguished between tactile ‘word’ sequences constructed from distinct vibrations delivered to the whiskers, assembled in different orders. Animals licked to report the presence of the target sequence. Optogenetic inactivation showed that the somatosensory cortex was necessary for sequence discrimination. Two-photon imaging in layer 2/3 of the primary somatosensory “barrel” cortex (S1bf) revealed that, in well-trained animals, neurons had heterogeneous selectivity to multiple task variables including not just sensory input but also the animal’s action decision and the trial outcome (presence or absence of the predicted reward). Many neurons were activated preceding goal-directed licking, thus reflecting the animal’s learnt action in response to the target sequence; these neurons were found as soon as mice learned to associate the rewarded sequence with licking. In contrast, learning evoked smaller changes in sensory response tuning: neurons responding to stimulus features were already found in naïve mice, and training did not generate neurons with enhanced temporal integration or categorical responses. Therefore, in S1bf sequence learning results in neurons whose activity reflects the learnt association between target sequence and licking, rather than a refined representation of sensory features. Taken together with results from other laboratories, our findings suggest that neurons in sensory cortex are involved in task-specific processing and that an animal does not sense the world independently of what it needs to feel in order to guide behaviour.

Sensory representations are typically thought as neuronal activity patterns that encode physical attributes of the outside world. However, increasing evidence is showing that as animals learned the association between a sensory stimulus and its behavioral relevance, stimulus representation in sensory cortical areas can change. In this seminar I will present recent experiments from our lab showing that the activity in the olfactory piriform cortex (PC) of mice encodes not only odor information, but also non-olfactory variables associated with the behavioral task. By developing an associative olfactory learning task, in which animals learn to associate a particular context with an odor and a reward, we were able to record the activity of multiple neurons as the animal runs in a virtual reality corridor. By analyzing the population activity dynamics using Principal Components Analysis, we find different population trajectories evolving through time that can discriminate aspects of different trial types. By using Generalized Linear Models we further dissected the contribution of different sensory and non-sensory variables to the modulation of PC activity. Interestingly, the experiments show that variables related to both sensory and non-sensory aspects of the task (e.g., odor, context, reward, licking, sniffing rate and running speed) differently modulate PC activity, suggesting that the PC adapt odor processing depending on experience and behavior.