Efficient Estimation of Phase-Resetting Curves in Real Neurons and its Significance for Neural-Network Modeling
Abstract
The phase-resetting curve (PRC) of a neural oscillator describes the effect of a perturbation on its periodic motion and is therefore useful to study how the neuron responds to stimuli and whether it phase locks to other neurons in a network. Combining theory, computer simulations and electrophysiological experiments we present a simple method for estimating the PRC of real neurons. This allows us to simplify the complex dynamics of a single neuron to a phase model. We also illustrate how to infer the existence of coherent network activity from the estimated PRC.
- Publication:
-
Physical Review Letters
- Pub Date:
- April 2005
- DOI:
- 10.1103/PhysRevLett.94.158101
- Bibcode:
- 2005PhRvL..94o8101G
- Keywords:
-
- 87.18.Sn;
- 05.45.Xt;
- 84.35.+i;
- 89.75.Hc;
- Neural networks;
- Synchronization;
- coupled oscillators;
- Neural networks;
- Networks and genealogical trees