PMID- 22684587
OWN - NLM
STAT- MEDLINE
DCOM- 20130411
LR  - 20240504
IS  - 1573-6873 (Electronic)
IS  - 0929-5313 (Print)
IS  - 0929-5313 (Linking)
VI  - 33
IP  - 3
DP  - 2012 Dec
TI  - Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs 
      reduce orientation selectivity.
PG  - 559-72
LID - 10.1007/s10827-012-0401-0 [doi]
AB  - One of the reasons the visual cortex has attracted the interest of computational 
      neuroscience is that it has well-defined inputs. The lateral geniculate nucleus 
      (LGN) of the thalamus is the source of visual signals to the primary visual 
      cortex (V1). Most large-scale cortical network models approximate the spike 
      trains of LGN neurons as simple Poisson point processes. However, many studies 
      have shown that neurons in the early visual pathway are capable of spiking with 
      high temporal precision and their discharges are not Poisson-like. To gain an 
      understanding of how response variability in the LGN influences the behavior of 
      V1, we study response properties of model V1 neurons that receive purely 
      feedforward inputs from LGN cells modeled either as noisy leaky 
      integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first 
      demonstrate that the NLIF model is capable of reproducing many experimentally 
      observed statistical properties of LGN neurons. Then we show that a V1 model in 
      which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces 
      higher orientation selectivity than the one with Poisson LGN input. The second 
      result implies that statistical characteristics of LGN spike trains are important 
      for V1's function. We conclude that physiologically motivated models of V1 need 
      to include more realistic LGN spike trains that are less noisy than inhomogeneous 
      Poisson processes.
FAU - Lin, I-Chun
AU  - Lin IC
AD  - Center for Neural Science, New York University, New York, NY 10003, USA. 
      icl231@nyu.edu
FAU - Xing, Dajun
AU  - Xing D
FAU - Shapley, Robert
AU  - Shapley R
LA  - eng
GR  - R01 EY001472/EY/NEI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20120610
PL  - United States
TA  - J Comput Neurosci
JT  - Journal of computational neuroscience
JID - 9439510
SB  - IM
MH  - Algorithms
MH  - Contrast Sensitivity
MH  - Electrophysiological Phenomena
MH  - Geniculate Bodies/*physiology
MH  - Humans
MH  - *Models, Neurological
MH  - Nerve Net/*physiology
MH  - Neural Conduction/physiology
MH  - Neurons/physiology
MH  - Normal Distribution
MH  - Orientation/*physiology
MH  - Photic Stimulation
MH  - Poisson Distribution
MH  - ROC Curve
MH  - Thalamus/physiology
MH  - Visual Cortex/*physiology
PMC - PMC4104821
MID - NIHMS600009
EDAT- 2012/06/12 06:00
MHDA- 2013/04/12 06:00
PMCR- 2014/07/21
CRDT- 2012/06/12 06:00
PHST- 2012/03/13 00:00 [received]
PHST- 2012/05/23 00:00 [accepted]
PHST- 2012/05/22 00:00 [revised]
PHST- 2012/06/12 06:00 [entrez]
PHST- 2012/06/12 06:00 [pubmed]
PHST- 2013/04/12 06:00 [medline]
PHST- 2014/07/21 00:00 [pmc-release]
AID - 10.1007/s10827-012-0401-0 [doi]
PST - ppublish
SO  - J Comput Neurosci. 2012 Dec;33(3):559-72. doi: 10.1007/s10827-012-0401-0. Epub 
      2012 Jun 10.