Gamma frequency synchronization in a local cortical network model

2004 ◽  
Vol 58-60 ◽  
pp. 173-178 ◽  
Author(s):  
Masaki Nomura ◽  
Toshio Aoyagi ◽  
Tomoki Fukai
Keyword(s):  
Network Model ◽  
Cortical Network ◽  
Frequency Synchronization ◽  
Gamma Frequency

scholarly journals Understanding the relationships between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model

NeuroImage ◽  
2010 ◽  
Vol 52 (3) ◽  
pp. 956-972 ◽  
Author(s):  
Alberto Mazzoni ◽  
Kevin Whittingstall ◽  
Nicolas Brunel ◽  
Nikos K Logothetis ◽  
Stefano Panzeri
Keyword(s):  
Network Model ◽  
Spike Rate ◽  
Cortical Network ◽  
Frequency Bands ◽  
Gamma Frequency

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

scholarly journals Inter-synaptic learning of combination rules in a cortical network model

2014 ◽  
Vol 5 ◽  
Author(s):  
Frédéric Lavigne ◽  
Francis Avnaïm ◽  
Laurent Dumercy
Keyword(s):  
Network Model ◽  
Cortical Network ◽  
Combination Rules ◽  
Synaptic Learning

A Cortical Network Model for Early Vision Processing

1989 ◽  
pp. 139-148
Author(s):  
P. Møller ◽  
M. Nylén ◽  
J. A. Hertz
Keyword(s):  
Network Model ◽  
Cortical Network ◽  
Early Vision ◽  
Vision Processing

scholarly journals Relationship between synaptic and functional connections of a local cortical network model

2007 ◽  
Vol 8 (S2) ◽  
Author(s):  
Katsunori Kitano ◽  
Kazuhiro Yamada ◽  
Tomoki Fukai
Keyword(s):  
Network Model ◽  
Cortical Network ◽  
Functional Connections

scholarly journals Rhythm and Synchrony in a Cortical Network Model

2018 ◽  
Vol 38 (40) ◽  
pp. 8621-8634 ◽  
Author(s):  
Logan Chariker ◽  
Robert Shapley ◽  
Lai-Sang Young
Keyword(s):  
Network Model ◽  
Cortical Network
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