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Relation between Single Neuron and Population Spiking Statistics and Effects on Network Activity
 

Summary: Relation between Single Neuron and Population Spiking Statistics and Effects
on Network Activity
Hideyuki Ca^teau1,2
and Alex D. Reyes1
1
Center for Neural Science, New York University, 4 Washington Place, New York, New York 10003, USA
2
RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
(Received 5 July 2005; published 6 February 2006)
To simplify theoretical analyses of neural networks, individual neurons are often modeled as Poisson
processes. An implicit assumption is that even if the spiking activity of each neuron is non-Poissonian, the
composite activity obtained by summing many spike trains limits to a Poisson process. Here, we show
analytically and through simulations that this assumption is invalid. Moreover, we show with Fokker-
Planck equations that the behavior of feedforward networks is reproduced accurately only if the tendency
of neurons to fire periodically is incorporated by using colored noise whose autocorrelation has a negative
component.
DOI: 10.1103/PhysRevLett.96.058101 PACS numbers: 87.19.La, 02.50.Ey, 05.10.Gg, 87.18.Sn
Neurons have a variety of ion channels that transduce
synaptic input into spiking output. Because the channels
have a wide range of activation time constants (submilli-

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Fukai, Tomoki - Brain Science Institute, RIKEN

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences