The Response Dynamics of Neural Oscillator Populations
by
Jeff Moehlis
University of California at Santa Barbara
Coauthors: Eric Brown (Princeton University), Phil Holmes (Princeton University)

We undertake a probabilistic analysis of the response of repetitively firing neural populations to simple pulselike stimuli. This work is motivated by experimental data which shows that neurons in a region of the brain known as the locus coeruleus (LC) can exhibit distinct firing patterns which are strongly correlated with performance on cognitive tasks. Using a phase oscillator model for the LC neurons, we compute average firing probabilities for a pool of neurons in response to stimuli over many trials. This involves the solution of an advection-diffusion equation, and shows that neural response (1) is elevated in populations with lower baseline firing rates, and (2) decays due to noise and distributions of neuron frequencies. Similar results are obtained for other types of neurons.

Date received: October 6, 2003