Structured chaos shapes spike-response noise entropy in balanced neural networks

Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. A striking feature of these networks is that they are chaotic. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for the entropy of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complimented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos," as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.Comment: 9 pages, 5 figure

Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination

Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be "room" for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10's of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks does not prohibit precise stimulus encoding.Comment: 8 figure

La responsabilité civile du médecin aux soins intensifs

Résumé: L'intensiviste est un médecin spécialiste dont l'activité clinique est dédiée aux malades en état critique séjournant aux soins intensifs . À ce titre, il entretient des rapports complexes et particuliers avec les malades et leurs famiiies, les médecins référents et consultants et le personnel soignant de l'Unité de Soins Intensifs (USI). De plus, il peut conduire ou participer à des activités de recherche clinique sur cette clientèle vulnérable dans le but d'accroître le savoir médical et ainsi améliorer leur pronostic de survie. Cet essai examinera les aspects juridiques, particulièrement la responsabilité civile du médecin aux soins intensifs.||Abstract: In modern intensive care, the physician specialized in the care of critically ill is called an intensivist. Furthermore, he has complex interrelations with patients and their family , with refering and consulting physicians as well as with the nursing staff of the intensive care unit. He can also conduct or participate in scholarly research activities, trying to increase the medical knowledge and improve the survival prognosis of this vulnerable population. This essay will discuss legal issues, particularly civil liability of the physician in intensive care

Évolution d'une ville: Rouen 1962-82

Pour une comparaison rigoureuse de l’espace rouennais, les auteurs proposent, au lieu du maillage changeant des îlots ou quartiers, un maillage fixe de carrés de 250 m de côté où les données sont transférées par calcul. Cette procédure («grid map» des Britanniques) autorise des traitements statistiques moins biaisés

Soutien au développement de ressources numériques pour l'enseignement et l'apprentissage dans les universités québécoises: Rapport complet

Tiré de l'écran-titre (visionné le 27 juin 2017).Ce portrait se veut une source d’information pour les professionnels et les décideurs engagés dans le soutien à l’enseignement et à l’apprentissage. Il est souhaité qu’il suscite des discussions et initie des réflexions auprès des intervenants préoccupés par l’offre de contenus numériques de qualité pour les étudiants