Neuronal network models of ADHD ? lateralization with respect to interhemispheric connectivity reconsidered

2004 ◽  
Vol 13 (S1) ◽  
Author(s):  
Veit Roessner ◽  
T. Banaschewski ◽  
H. Uebel ◽  
A. Becker ◽  
A. Rothenberger
Keyword(s):  
Neuronal Network ◽  
Network Models ◽  
Interhemispheric Connectivity

scholarly journals Towards Reproducible Descriptions of Neuronal Network Models

2009 ◽  
Vol 5 (8) ◽  
pp. e1000456 ◽  
Author(s):  
Eilen Nordlie ◽  
Marc-Oliver Gewaltig ◽  
Hans Ekkehard Plesser
Keyword(s):  
Neuronal Network ◽  
Network Models

EVANS FUNCTIONS AND NONLINEAR STABILITY OF TRAVELING WAVES IN NEURONAL NETWORK MODELS

2007 ◽  
Vol 17 (08) ◽  
pp. 2693-2704 ◽  
Author(s):  
BJÖRN SANDSTEDE
Keyword(s):  
Differential Equations ◽  
Neuronal Network ◽  
Nonlinear Stability ◽  
Traveling Waves ◽  
Network Models ◽  
Function Spaces ◽  
Spectral Stability ◽  
Evans Function ◽  
Evans Functions

Modeling networks of synaptically coupled neurons often leads to systems of integro-differential equations. Particularly interesting solutions in this context are traveling waves. We prove here that spectral stability of traveling waves implies their nonlinear stability in appropriate function spaces, and compare several recent Evans-function constructions that are useful tools when analyzing spectral stability.

Modularity-dependent modulation of synchronized bursting activity in cultured neuronal network models

2017 ◽  
Author(s):  
Satoshi Moriya ◽  
Hideaki Yamamoto ◽  
Hisanao Akima ◽  
Ayumi Hirano-Iwata ◽  
Michio Niwano ◽  
...  
Keyword(s):  
Neuronal Network ◽  
Network Models ◽  
Bursting Activity

scholarly journals Motor primitives in space and time via targeted gain modulation in cortical networks

2018 ◽  
Author(s):  
Jake P. Stroud ◽  
Mason A. Porter ◽  
Guillaume Hennequin ◽  
Tim P. Vogels
Keyword(s):  
Neuronal Network ◽  
Learning Rule ◽  
Network Models ◽  
Network Activity ◽  
Movement Speed ◽  
Control Units ◽  
Dimensional Network ◽  
Wide Range ◽  
New Perspective

AbstractMotor cortex (M1) exhibits a rich repertoire of activities to support the generation of complex movements. Although recent neuronal-network models capture many qualitative aspects of M1 dynamics, they can generate only a few distinct movements. Additionally, it is unclear how M1 efficiently controls movements over a wide range of shapes and speeds. We demonstrate that simple modulation of neuronal input–output gains in recurrent neuronal-network models with fixed architecture can dramatically reorganize neuronal activity and thus downstream muscle outputs. Consistent with the observation of diffuse neuromodulatory projections to M1, we show that a relatively small number of modulatory control units provide sufficient flexibility to adjust high-dimensional network activity using a simple reward-based learning rule. Furthermore, it is possible to assemble novel movements from previously learned primitives, and one can separately change movement speed while preserving movement shape. Our results provide a new perspective on the role of modulatory systems in controlling recurrent cortical activity.

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