Time scales of spike-train correlation for neural oscillators with common drive
- Department of Applied Mathematics, University of Washington, P.O. Box 352420, Seattle, Washington 98195 (United States)
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.
- OSTI ID:
- 21344699
- Journal Information:
- Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics (Print), Vol. 81, Issue 1; Other Information: DOI: 10.1103/PhysRevE.81.011916; (c) 2010 The American Physical Society; ISSN 1539-3755
- Country of Publication:
- United States
- Language:
- English
Similar Records
Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule
Energy Efficient Stochastic-Based Deep Spiking Neural Networks for Sparse Datasets