TY - GEN

T1 - Analytical solution for dynamic of neuronal populations

AU - Huang, Wentao

AU - Jiao, Licheng

AU - Ma, Shiping

AU - Xu, Yuelei

PY - 2005/12/1

Y1 - 2005/12/1

N2 - The population density approach is a viable method to describe the large populations of neurons and has generated considerable interest recently. The evolution in time of the population density is determined by a partial differential equation. Now, the discussion of most researchers is based on the population density function. In this paper, we propose a new function to characterize the population of excitatory and inhibitory spiking neurons and derive a novel evolution equation which is a nonhomogeneous parabolic type equation. Moreover, we study the stationary solution and give the firing rate of the stationary states. Then we solve for the time dependent solution using the Fourier transform, which can be used to analyze the various behavior of cerebra.

AB - The population density approach is a viable method to describe the large populations of neurons and has generated considerable interest recently. The evolution in time of the population density is determined by a partial differential equation. Now, the discussion of most researchers is based on the population density function. In this paper, we propose a new function to characterize the population of excitatory and inhibitory spiking neurons and derive a novel evolution equation which is a nonhomogeneous parabolic type equation. Moreover, we study the stationary solution and give the firing rate of the stationary states. Then we solve for the time dependent solution using the Fourier transform, which can be used to analyze the various behavior of cerebra.

UR - http://www.scopus.com/inward/record.url?scp=33646170888&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33646170888&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33646170888

SN - 3540287523

SN - 9783540287520

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 19

EP - 24

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005

Y2 - 11 September 2005 through 15 September 2005

ER -