At any moment in one’s day, we are confronted with a seemingly endless array of stimuli, or sensory events, and a correspondingly large number of choices. Based on incoming traffic, for instance, you might (wisely) decide to not attempt to cross the street. In this case, the stimuli impinging upon your senses include auditory (eg cars honking, or slightly angry folk cursing you out), visual (eg cars flashing their lights, or slightly angry folks making obscene gestures), somatosensory (eg if you feel a car, you may have made a poor decision), and that of any of your other favorite senses.

We know that, at some point, our brains use this sensory information to inform our decisions to commit, or not commit, an action – such as walking across the street, or staying put; in short, whether to initiate, or suppress, movement. During this almost unconscious process, neurons in primary motor cortex (M1), a region of the brain intimately involved in the learning and execution of movements, demonstrate different activity patterns. Although others have suggested that these different patterns may simply reflect different task or movement parameters, David McCormick and his lab propose that these neurons are interacting in order to control the initiation of movement. Specifically, control over movement initiation or suppression may depend on inhibition, although it is unknown how the brain implements this form of behavioral control.

To get at this mechanism, Zagha et al. (2015) first trained mice until they became veritable experts in a simple task – water-restricted head-fixed mice had their whiskers deflected, termed the ’target,’ and the mice then had to lick for the trial to be considered ‘correct’. In 80% of trials there was also a tone presented before the whiskers were deflected, and in this case the mice had to withhold licking until their whiskers were deflected; at that point, licking would allow them to receive their reward (i.e. water; Fig1A, C). These trials were particularly important, as they discouraged impulsivity, as evidenced by the higher hit rate and lower false alarm rate in expert mice compared to novices (Fig1E, F ). Since expert mice were not impulsive, the authors could look at M1 neurons as the mouse was making a conscious decision to not lick (ie a suppression of movement).


Fig 1 – Mice become experts! A) The setup B) With more obvious stimuli (i.e. faster speed), the mouse performs better C) Task structure D) Successful trial, with licking after target onset E, F) Mice do better! G) … Unless you inhibit M1

Curiously, by silencing M1 via injection of muscimol, a GABAA agonist, or via optogenetic activation of PV-containing GABAergic interneurons, Zagha et al. noticed that expert mice would have many more false alarms (Fig1G). This implied that M1 is involved in the suppression of movements.

To figure out what was going on in M1, the authors of the study then used loose patch or multi-electrode arrays to record from neurons in layer 5 of M1. They noticed that some neurons had an increase in spike rate after stimulus onset, but before whisking or licking (Fig2A, C), and another population had a decrease in spike rate for the same period of time (Fig2B, D); simultaneous recordings told them that these two populations of neurons co-occurred (Fig2E). By looking at the change in firing rates across the target stimulus on ‘hit’, or correct, trials, they found two populations of neurons, fitted by two gaussians – one with spike rate enhancement, and another with suppression (Fig2I), further supporting their claim of two distinct populations of neurons in M1.


Fig 2 – A,C) Some neurons become more active (i.e. enhanced population) B,D) Some neurons become less active (i.e. suppressed population) E) Both populations co-occur during a task F-I) By fitting to Gaussians, we see two distinct populations of neurons (blue and red Gaussians)

Because these neurons could also be active during other epochs of the task, they might not actually represent the anticipation of movement, or the formation of a decision to (not) move. The authors addressed this by looking at the average neural activity of target-modulated neurons across different conditions and epochs of the task. Their activity was stable across tone presentation, which implied that they aren’t representing a sensory response (Fig3A, F). However, after presentation of the target, one population had a sustained increase in activity, and the other a decrease (Fig3B, G). On ‘miss’ trials, these changes in activity were absent or less pronounced (Fig3C, H), and both of these populations of neurons ramped up, or down, their activity in anticipation of licking, even in off-target responses (i.e. false alarms and spontaneous licking; Fig3D, E, I, J). All of this suggested that these two populations of neurons represent an upcoming motor choice.


Fig 3 – A,F) Our two populations of target-modulated neurons aren’t modulated by the tone B,G) …But they are modulated by the target C,H) …And you see less modulation when the mouse was incorrect on that trial D,I) They can also be modulated in anticipation of an action (i.e. licking) E,J)…Even when the movement is incorrect

Since it seemed that these neurons were involved in the anticipation of movement, and their activity was anti-correlated (Fig4F), the authors next investigated the possibility that they inhibited one another, as that is one possible mechanism that could produce anti-correlated activity. Simulations verified that lateral inhibition could produce anti-correlation (Fig4A-D), and the authors, by using Granger causality, found that the enhanced firing (Enh) and suppressed firing (Supp) populations inhibited one another, and that this could be behind their anti-correlated activity (Fig4D, F). Furthermore, the authors simulated their competitive ensemble model (Fig5A), and found that a transient stimulus could be turned into a sustained response due to its intrinsic dynamics (Fig5B). In fact, their simulated data was qualitatively similar to the neural data they observed (Fig5C-F).


Fig 4 – A-D) Four different two-ensemble circuit models. You can get anti-correlated activity by either anti-correlated inputs (B), or lateral inhibition (C,D), or both. E) The Enh and Supp populations are anti-correlated F)…And it seems that mutual inhibition is to blame

The authors also found that some neurons were modulated by the target only, by the motor command only, or by both the target and the motor command. This motor-specific population of neurons displayed ramping activity, which could be used to trigger movement if a movement command is issued only when the firing activity of the population reaches a bound (this process is called accumulation-to-bound; Fig6A-F). Since these neurons modulated their activity late in the decision process, they were likely not involved in the aforementioned state transitions (Fig5B).



Fig 5 – A) Competitive ensemble model B) Phase plan shows how a transient stimulus can lead to a miss or hit C,D) Two simulations, which depict how both ensembles’ firing activity change when the stimulus does (red or blue) or does not (black) lead to a stable transition E,F) It’s cool that the neural data seems to follow along with the simulations



Fig 6 – A-C) Some neurons have enhanced activity, where the peak activity is aligned to the target, and likely act as sensory representations. Note how the timing of their peak activity is unrelated to the mouse’s reaction time (RT) D-F) Other neurons show ramping activity, where the peak is aligned to movement, not the target. Here, RT does matter, with earlier peaks for quicker RTs

Therefore, it seems that M1 neurons, with different activities in anticipation of movement, inhibit one another, and can use a transient stimulus to transition from one state of their circuit to another. The authors suggest that these ‘sensory’ neurons can then drive ramping activity in motor-enhanced neurons to trigger a movement, and this could be carried out via the sequential activation of overlapping populations of neurons. The authors carried out further analyses, and found that slow, oscillatory dynamics were correlated with poor performance in the task, seemingly by disrupting anti-correlated activity between the two populations of target-modulated neurons. The mechanisms by which oscillations disrupt anti-correlation, however, remains to be addressed.


In short, it should be worth it to come to the CNCB Marilyn Farquhar Seminar Room, on Tuesday, 3/1/16, at 4.00 pm, to hear Dr David McCormick give his talk titled “Neural mechanisms of optimal state”

The article which this post was based on is here. Enjoy!


Javier How is a 1st year student currently rotating in Dr Takaki Komiyama’s lab, where he learns about learning. Due to blistering feedback, he no longer writes haikus about complex topics, as evidenced by this little gem

My name is Javier,

I no longer write haikus

I was told to stop


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