PMID- 12662653
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20191120
IS  - 1879-2782 (Electronic)
IS  - 0893-6080 (Linking)
VI  - 12
IP  - 7-8
DP  - 1999 Oct
TI  - Temporally correlated inputs to leaky integrate-and-fire models can reproduce 
      spiking statistics of cortical neurons.
PG  - 1181-1190
AB  - There has been controversy over whether the standard neuro-spiking models are 
      consistent with the irregular spiking of cortical neurons. In a previous study, 
      we proposed examining this consistency on the basis of the high-order statistics 
      of the inter-spike intervals (ISIs), as represented by the coefficient of 
      variation and the skewness coefficient. In that study we found that a leaky 
      integrate-and-fire model incorporating the assumption of temporally uncorrelated 
      inputs is not able to account for the spiking data recorded from a monkey 
      prefrontal cortex. In the present paper, we attempt to revise the neuro-spiking 
      model so as to make it consistent with the biological data. Here we consider the 
      correlation coefficient of consecutive ISIs, which was ignored in previous 
      studies. Considering three statistical coefficients, we conclude that the leaky 
      integrate-and-fire model with temporally correlated inputs does account for the 
      biological data. The correlation time scale of the inputs needed to explain the 
      biological statistics is found to be on the order of 100ms. We discuss possible 
      origins of this input correlation.
FAU - Sakai, Y
AU  - Sakai Y
AD  - Department of Physics, Graduate School of Science, Kyoto University, Sakyo-ku, 
      Kyoto, Japan
FAU - Funahashi, S
AU  - Funahashi S
FAU - Shinomoto, S
AU  - Shinomoto S
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Neural Netw
JT  - Neural networks : the official journal of the International Neural Network 
      Society
JID - 8805018
EDAT- 2003/03/29 05:00
MHDA- 2003/03/29 05:01
CRDT- 2003/03/29 05:00
PHST- 2003/03/29 05:00 [pubmed]
PHST- 2003/03/29 05:01 [medline]
PHST- 2003/03/29 05:00 [entrez]
AID - S0893-6080(99)00053-2 [pii]
AID - 10.1016/s0893-6080(99)00053-2 [doi]
PST - ppublish
SO  - Neural Netw. 1999 Oct;12(7-8):1181-1190. doi: 10.1016/s0893-6080(99)00053-2.