Neural coding in primary motor cortex

My first research experience was as an undergraduate in John Donoghue's lab, under the guidance of Nicho Hatsopoulos. I helped analyze some simultaneous recordings of neurons in primary motor cortex and helped design a behavioral task to study on-line planning of sequential movements.

Hatsopoulos, N., Ojakangas, C., Paninski, L. & Donoghue, J. (1998). Information about movement direction obtained from synchronous activity of motor cortical neurons. PNAS 95: 15706-11.

Hatsopoulos, N., Paninski, L. & Donoghue, J. (2003). Sequential movement representations based on correlated neuronal activity. Experimental Brain Research 149: 478-486.


While working on the problem of analyzing pairwise interactions between neurons, we became more interested in understanding single neuronal responses. We designed a novel random-pursuit behavioral task to obtain better measurements of motor cortical neurons' "spatiotemporal" response properties. At the same time, we became interested in methods for decoding this neural activity into an estimate of the ongoing dynamic hand position during behavior. This decoding problem has close connections with the design of neural prosthetic devices.

Serruya, M., Hatsopulos, N., Paninski, L., Fellows, M. & Donoghue, J. (2002). Brain-machine interface: instant neural control of a movement signal. Nature 416: 141-2.

Paninski, L., Shoham, S., Fellows, M., Hatsopoulos, N. & Donoghue, J. (2004). Superlinear population encoding of dynamic hand trajectory in primary motor cortex. Journal of Neuroscience 24: 8551-8561.

Shoham, S., Paninski, L., Fellows, M., Hatsopoulos, N., Donoghue, J. & Normann, R. (2005). Optimal encoding model for a primary motor cortical brain-computer interface. IEEE Transactions on Biomedical Engineering 52: 1312-1322.

Townsend, B., Paninski, L. & Lemon, R. (2006). Linear encoding of muscle activity in primary motor cortex and cerebellum. J. Neurophys. 96: 2578-92.

Kulkarni, J. & Paninski, L. (2008). Efficient analytic computational methods for state-space decoding of goal-directed movements. IEEE Signal Processing Magazine 25 (special issue on brain-computer interfaces): 78-86.

Wu, W., Kulkarni, J., Hatsopoulos, N. & Paninski, L. (2009). Neural decoding of goal-directed movements using a linear state-space model with hidden states. IEEE Trans. Neural Systems and Rehabilitation Engineering 17: 370-378.

Lawhern, V., Wu, W., Hatsopoulos, N. & Paninski, L. (2010). Population neuronal decoding using a generalized linear model with hidden states. In press, J. Neuroscience Methods.


Liam Paninski's research