Laboratory of Mark M. Churchland
Your brain, and the neurons within it, respond to external stimuli such as a friend’s face or voice. At the other extreme, the final output of your brain is a set of commands sent to your muscles. Yet most of the brain’s activity is neither a reflexive response nor a direct motor command. The brain sustains and generates its own activity, and this is at the heart of the remarkable feats it can accomplish.
A central goal of our laboratory is to understand the neural dynamics that allow the brain to generate its own activity. We approach this problem in the context of voluntary movement. Voluntary movement requires a series of internally generated events that must unfold over time before the overt movement is produced. This gives us a unique opportunity to study activity that is internally generated but still relates to measureable events (e.g., the speed or accuracy of a movement).
We take a dynamical systems approach to understanding the neural events that drive movement. We are particularly interested in uncovering the ‘rules of neural motion.’ In this view, the right way to understand internally generated activity is to decipher how and why the neural ‘state’ at one moment in time leads to the neural state at the next moment in time.
Our belief is that an understanding of neural dynamics will shed a great deal of light on how the brain generates and controls movement normally, and on how this process can go awry in disease.
- Feb 2: Press coverage for Cortical activity in the null space: permitting preparation without movement:
- May: Mark Churchland receives a 2013-2015 McKnight Scholar Award.
- March 29: Jeffrey Seely awarded an NSF fellowship.
- February 21: Mark Churchland named an Alfred P. Sloan Foundation Fellow.
- November 1: Mark Churchland lab featured in MBBI newsletter.
- October 11: Grossman Center for the Statistics of Mind launched, Drs. Mark Churchland and Liam Paninski named co-directors.
- Sept 14: Jeffrey Seely receives the Brains for Brains Young Researchers' Computational Neuroscience Award at the Bernstein Conference in Munich.
- Sept 13: Mark Churchland receives 2012 NIH Director's New Innovator Award.
- June 5: Press coverage for Structure of neural population dynamics during reaching:
- June 3: Structure of neural population dynamics during reaching is published in Nature. [link]
- April 13: Mark named 2012 Searle Scholar.
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. For example, it is often suggested that neurons in motor cortex should relate in a faithful way to external movement parameters such as the direction, distance and speed of a reach. Many experiments have sought such lawfulness, yet none have found it.
We suggest that a better analogy might be with other motor systems, where a common principle is rhythmic neural activity. We found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behavior. 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.
It therefore seems increasing likely that motor cortex can be understood in relatively straightforward terms: as an engine of movement that uses lawful dynamics. Yet why would motor cortex employ rhythmic activity during a non-rhythmic reaching movement? The answer appears to be that muscle activity, even for non-rhythmic movements, can be constructed from briefly rhythmic components (see figure).