Extracting Dynamical Structure Embedded in Neural Activity

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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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.