Time scales of spike-train correlation for neural oscillators with common drive.
We examine the effect of the phase-resetting curve on the transfer of correlated input signals into correlated output spikes in a class of neural models receiving noisy superthreshold stimulation. We use linear-response theory to approximate the spike correlation coefficient in terms of moments of the associated exit time problem and contrast the results for type I vs type II models and across the different time scales over which spike correlations can be assessed. We find that, on long time scales, type I oscillators transfer correlations much more efficiently than type II oscillators. On short time scales this trend reverses, with the relative efficiency switching at a time scale that depends on the mean and standard deviation of input currents. This switch occurs over time scales that could be exploited by downstream circuits.
Barreiro
Andrea K
AK
Department of Applied Mathematics, University of Washington, Seattle, Washington 98195, USA. akb6@washington.edu
Shea-Brown
Eric
E
Thilo
Evan L
EL
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D016014:Linear Models; D008959:Models, Neurological; D009010:Monte Carlo Method; D009474:Neurons / Q000502:physiology*; D010507:Periodicity; D013997:Time Factors