International audienceCharacterizing the influence of network properties on the global emerging behavior of interacting elements constitutes a central question in many areas, from physical to social sciences. In this article we study a primary model of disordered neuronal networks with excitatory-inhibitory structure and balance constraints. We show how the interplay between structure and disorder in the connectivity leads to a universal transition from trivial to synchronized stationary or periodic states. This transition cannot be explained only through the analysis of the spectral density of the connectivity matrix. We provide a low-dimensional approximation that shows the role of both the structure and disorder in the dynamics
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent con-nectivity can amplify the input given to...
Using a generalized random recurrent neural network model, and by extending our recently developed m...
International audienceCharacterizing the influence of network properties on the global emerging beha...
International audienceCharacterizing the influence of network properties on the global emerging beha...
International audienceCharacterizing the influence of network properties on the global emerging beha...
International audienceCharacterizing the influence of network properties on the global emerging beha...
International audienceCharacterizing the influence of network properties on the global emerging beha...
Synchronization is an emergent property in networks of interacting dynamical elements. Here we revie...
Large sparse circuits of spiking neurons exhibit a balanced state of highly irregular activity under...
Large sparse circuits of spiking neurons exhibit a balanced state of highly irregular activity under...
Large sparse circuits of spiking neurons exhibit a balanced state of highly irregular activity under...
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent con-nectivity can amplify the input given to...
Using a generalized random recurrent neural network model, and by extending our recently developed m...
International audienceCharacterizing the influence of network properties on the global emerging beha...
International audienceCharacterizing the influence of network properties on the global emerging beha...
International audienceCharacterizing the influence of network properties on the global emerging beha...
International audienceCharacterizing the influence of network properties on the global emerging beha...
International audienceCharacterizing the influence of network properties on the global emerging beha...
Synchronization is an emergent property in networks of interacting dynamical elements. Here we revie...
Large sparse circuits of spiking neurons exhibit a balanced state of highly irregular activity under...
Large sparse circuits of spiking neurons exhibit a balanced state of highly irregular activity under...
Large sparse circuits of spiking neurons exhibit a balanced state of highly irregular activity under...
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent connectivity can amplify the input given to ...
In dynamical models of cortical networks, the recurrent con-nectivity can amplify the input given to...
Using a generalized random recurrent neural network model, and by extending our recently developed m...