Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)
Byron M. Yu, Afsheen Afshar, Gopal Santhanam, Stephen Ryu, Krishna V. Shenoy, Maneesh Sahani
Spiking activity from neurophysiological experiments often exhibits dy- namics beyond that driven by external stimulation, presumably reflect- ing the extensive recurrence of neural circuitry. Characterizing these dynamics may reveal important features of neural computation, par- ticularly during internally-driven cognitive operations. For example, the activity of premotor cortex (PMd) neurons during an instructed de- lay period separating movement-target specification and a movement- initiation cue is believed to be involved in motor planning. We show that the dynamics underlying this activity can be captured by a low- dimensional non-linear dynamical systems model, with underlying re- current structure and stochastic point-process output. We present and validate latent variable methods that simultaneously estimate the system parameters and the trial-by-trial dynamical trajectories. These meth- ods are applied to characterize the dynamics in PMd data recorded from a chronically-implanted 96-electrode array while monkeys perform delayed-reach tasks.