Spiking activity from neurophysiological experiments often exhibits dynamics beyond that driven by external stimulation, presumably reflecting the extensive recurrence of neural circuitry. Characterizing these dynamics may reveal important features of neural computation, particularly during internally-driven cognitive operations. For example, the activity of premotor cortex (PMd) neurons during an instructed delay period separating movement-target specification and a movementinitiation cue is believed to be involved in motor planning. We show that the dynamics underlying this activity can be captured by a lowdimensional nonlinear dynamical systems model, with underlying recurrent structure and stochastic point-process output.

Spiking activity from neurophysiological experiments often exhibits dynamics beyond that driven by external stimulation, presumably reflecting the extensive recurrence of neural circuitry. Characterizing these dynamics may reveal important features of neural computation, particularly during internally-driven cognitive operations. For example, the activity of premotor cortex (PMd) neurons during an instructed delay period separating movement-target specification and a movementinitiation cue is believed to be involved in motor planning. We show that the dynamics underlying this activity can be captured by a lowdimensional nonlinear dynamical systems model, with underlying recurrent structure and stochastic point-process output.

Spiking activity from neurophysiological experiments often exhibits dynamics beyondthat driven by external stimulation, presumably reflecting the extensive recurrence of neural circuitry. Characterizing these dynamics may reveal important features of neural computation, particularly duringinternally-driven cognitive operations. For example, the activity of premotor cortex (PMd) neurons during an instructed delay periodseparating movement-target specification and a movementinitiation cueis believed to be involved in motor planning. We show that the dynamics underlying this activity can be captured by a lowdimensional non-lineardynamical systems model, with underlying recurrent structure and stochastic point-process output.