Functional Networks Based on Pairwise Spike Synchrony Can Capture Topologies of Synaptic Connectivity in a Local Cortical Network Model

2009 ◽  
pp. 978-985 ◽  
Author(s):  
Katsunori Kitano ◽  
Kazuhiro Yamada
Keyword(s):  
Network Model ◽  
Cortical Network ◽  
Synaptic Connectivity ◽  
Functional Networks ◽  
Spike Synchrony

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 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 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

scholarly journals Large-scale Cortical Network Properties Predict Future Sound-to-Word Learning Success

2012 ◽  
Vol 24 (5) ◽  
pp. 1087-1103 ◽  
Author(s):  
John Patrick Sheppard ◽  
Ji-Ping Wang ◽  
Patrick C. M. Wong
Keyword(s):  
Word Learning ◽  
Large Scale ◽  
Functional Organization ◽  
Temporal Cortex ◽  
Spoken Language ◽  
Cortical Network ◽  
Functional Networks ◽  
Network Organization ◽  
Learning Abilities ◽  
Network Properties

The human brain possesses a remarkable capacity to interpret and recall novel sounds as spoken language. These linguistic abilities arise from complex processing spanning a widely distributed cortical network and are characterized by marked individual variation. Recently, graph theoretical analysis has facilitated the exploration of how such aspects of large-scale brain functional organization may underlie cognitive performance. Brain functional networks are known to possess small-world topologies characterized by efficient global and local information transfer, but whether these properties relate to language learning abilities remains unknown. Here we applied graph theory to construct large-scale cortical functional networks from cerebral hemodynamic (fMRI) responses acquired during an auditory pitch discrimination task and found that such network properties were associated with participants' future success in learning words of an artificial spoken language. Successful learners possessed networks with reduced local efficiency but increased global efficiency relative to less successful learners and had a more cost-efficient network organization. Regionally, successful and less successful learners exhibited differences in these network properties spanning bilateral prefrontal, parietal, and right temporal cortex, overlapping a core network of auditory language areas. These results suggest that efficient cortical network organization is associated with sound-to-word learning abilities among healthy, younger adults.

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
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