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Documents 35Q89 5 résultats

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We investigate the mean-field limit of large networks of interacting biological neurons. The neurons are represented by the so-called integrate and fire models that follow the membrane potential of each neuron and captures individual spikes. However we do not assume any structure on the graph of interactions but consider instead any connection weights between neurons that obey a generic mean-field scaling. We are able to extend the concept of extended graphons, introduced in Jabin-Poyato-Soler, by introducing a novel notion of discrete observables in the system. This is a joint work with D. Zhou.[-]
We investigate the mean-field limit of large networks of interacting biological neurons. The neurons are represented by the so-called integrate and fire models that follow the membrane potential of each neuron and captures individual spikes. However we do not assume any structure on the graph of interactions but consider instead any connection weights between neurons that obey a generic mean-field scaling. We are able to extend the concept of ...[+]

35Q49 ; 35Q83 ; 35R02 ; 35Q70 ; 05C90 ; 60G09 ; 35R06 ; 35Q89 ; 35Q92 ; 49N80 ; 92B20 ; 65N75

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We investigate the mean-field limit of large networks of interacting biological neurons. The neurons are represented by the so-called integrate and fire models that follow the membrane potential of each neuron and captures individual spikes. However we do not assume any structure on the graph of interactions but consider instead any connection weights between neurons that obey a generic mean-field scaling. We are able to extend the concept of extended graphons, introduced in Jabin-Poyato-Soler, by introducing a novel notion of discrete observables in the system. This is a joint work with D. Zhou.[-]
We investigate the mean-field limit of large networks of interacting biological neurons. The neurons are represented by the so-called integrate and fire models that follow the membrane potential of each neuron and captures individual spikes. However we do not assume any structure on the graph of interactions but consider instead any connection weights between neurons that obey a generic mean-field scaling. We are able to extend the concept of ...[+]

35Q49 ; 35Q83 ; 35R02 ; 35Q70 ; 05C90 ; 60G09 ; 35R06 ; 35Q89 ; 49N80 ; 92B20 ; 65N75 ; 65N75

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Mean Field Games - lecture 1 - Cardaliaguet, Pierre (Auteur de la conférence) | CIRM H

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The lecture is a short presentation of the theory of Mean Field Games (MFG) and Mean Field Control (MFC). After explaining how to derive these models from optimal control problems and games with a large number of players, we will describe the basic results of MFG (existence, uniqueness of the solution) and MFC, writing in the later case the associated infinite dimensional Hamilton-Jacobi equation and the optimality conditions.

35Q89 ; 49J55 ; 49K20

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Mean Field Games - lecture 2 - Cardaliaguet, Pierre (Auteur de la conférence) | CIRM H

Multi angle

The lecture is a short presentation of the theory of Mean Field Games (MFG) and Mean Field Control (MFC). After explaining how to derive these models from optimal control problems and games with a large number of players, we will describe the basic results of MFG (existence, uniqueness of the solution) and MFC, writing in the later case the associated infinite dimensional Hamilton-Jacobi equation and the optimality conditions.

35Q89 ; 93E20 ; 49K20

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Mean Field Games - lecture 3 - Cardaliaguet, Pierre (Auteur de la conférence) | CIRM H

Multi angle

The lecture is a short presentation of the theory of Mean Field Games (MFG) and Mean Field Control (MFC). After explaining how to derive these models from optimal control problems and games with a large number of players, we will describe the basic results of MFG (existence, uniqueness of the solution) and MFC, writing in the later case the associated infinite dimensional Hamilton-Jacobi equation and the optimality conditions.

35Q89 ; 93E20 ; 49K20

Sélection Signaler une erreur