Our lab studies how the brain generates voluntary movement. We record the spiking activity of 100’s of neurons simultaneously during a range of trained movements. We then attempt to decipher how the nervous system is generating those movements.
Our hypotheses and analyses are often guided by a dynamical systems perspective, in which we ask questions of the following sort:
How does the neural 'state' at one point in time lead to the neural state at the next point in time? What are the neural dynamics that allow the circuit as a whole to produce a coordinated pattern of activity that drives movement?
What is the role of movement 'planning' in the production of voluntary movement? Various lines of evidence, going back many years, indicate that voluntary movements are planned before they are executed. But what does 'planning' mean? Does it mean the programmatic and explicit selection of different movement 'parameters' (as a computer program might do)? Or does it mean the setting of the right 'initial dynamical state', akin to winding up a mechanical toy in just the right way before letting it go?
Why are movements variable? Does movement variability arise from variability in neural dynamics? From variability in movement planning?
How do the dynamics of normal movement go awry during disease? For example, does Parkinson’s disease involve a distortion of neural dynamics?
How (and how quickly) can we leverage our understanding of neural dynamics to improve the performance of neural prosthetic systems?
A selection of some recent and past projects
Neural population dynamics during reaching (Nature, 2012) [link]
Abstract: Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.
Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine? (Neuron, 2010) [pdf] [link]
Abstract: The motor cortices are active during both movement and movement preparation. A common assumption is that preparatory activity constitutes a subthreshold form of movement activity: a neuron active during rightward movements becomes modestly active during preparation of a rightward movement. We asked whether this pattern of activity is, in fact, observed. We found that it was not: at the level of a single neuron, preparatory tuning was weakly corre- lated with movement-period tuning. Yet, somewhat paradoxically, preparatory tuning could be captured by a preferred direction in an abstract ‘‘space’’ that described the population-level pattern of movement activity. In fact, this relationship accounted for prepa- ratory responses better than did traditional tuning models. These results are expected if preparatory activity provides the initial state of a dynamical system whose evolution produces movement activity. Our results thus suggest that preparatory activity may not represent specific factors, and may instead play a more mechanistic role.
This was a collaboration that involved data from the Newsome, Bradley, Kohn, Movshon, Moore, Snyder, Lisberger, and Ferster laboratories.
Abstract: Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.
From the Introduction: In 1990, Larry Bird made 71 consecutive free throws across almost two month’s worth of games. While this is a remarkable feat, one cannot help but wonder: why did he miss the 72nd? Why could he not simply do what he had done the last 71 times? As humans, we take for granted that our behavior is variable, and that repeated attempts will have variable results, but what is the source of this variability? When we err, we often assume that something went wrong during the movement. But might variability also arise during motor preparation, well be- fore the first muscle contracts?
Delay of Movement Caused by Disruption of Cortical Preparatory Activity (J. Neurophys, 2007) [pdf] [link]
We tested the hypothesis that delay-period activity in premotor cortex is essential to movement preparation. During a delayed-reach task, we used subthreshold intracortical microstimulation to disrupt putative “prepatory” activity. Microstimulation led to a highly specific increase in reach reaction time. Effects were largest when activity was disrupted around the time of the go cue. Earlier disruptions, which presumably allowed movement preparation time to recover, had only a weak impact. Furthermore, saccadic reaction time showed little or no increase. Finally, microstimulation of nearby primary motor cortex, even when slightly suprathreshold, had little effect on reach reaction time. These findings provide the first evidence, of a causal and temporally specific nature, that activity in premotor cortex is fundamental to movement preparation. Furthermore, although reaction times were increased, the movements themselves were essentially unperturbed. This supports the suggestion that movement preparation is an active and actively monitored process and that movement can be delayed until inaccuracies are repaired. These results are readily interpreted in the context of the recently developed optimal-subspace hypothesis.
Shifts in the Population Response in the Middle Temporal Visual Area Parallel Perceptual and Motor Illusions Produced by Apparent Motion (J. Neurosci., 2001) [pdf] [link]
We recorded behavioral, perceptual, and neural responses to targets that provided apparent visual motion consisting of a sequence of stationary flashes. Increasing the flash separation degrades the quality of motion, but for some separations evoked larger smooth pursuit responses from both humans and monkeys than did smooth motion. The same flash separations also produced an increase in perceived speed in humans. Recordings from single neurons in the middle temporal visual area (MT) of awake monkeys revealed a potential basis for the illusion in the population response. Apparent motion produced diminished neural responses relative to smooth motion. How- ever, neurons with slow preferred speeds were more affected than were those with fast preferred speeds. Increasing the flash separation thus caused the population response to become diminished in amplitude and to shift so that the most active neurons had higher preferred speeds. The entire constellation of effects of apparent motion on the magnitude and latency of the initial pursuit response was accounted for if the MT popu- lation response was decoded by (1) creating an opponent motion signal for each neuron by treating its preferred and opposite direction responses as those of a pair of oppositely tuned neurons and (2) computing the vector average of these opponent motion signals. Other ways of decoding the popula- tion response recorded in MT failed to account for one or more aspects of behavior. We conclude that the effects of apparent motion on both pursuit and perception can be accounted for if target speed is estimated from the MT population response by a neural computation that implements a vector average based on opponent motion.