Response-dependent dynamics of cell-specific inhibition in cortical networks in vivo

In the visual cortex, inhibitory neurons alter the computations performed by target cells via combination of two fundamental operations, division and subtraction. The origins of these operations have been variously ascribed to differences in neuron classes, synapse location or receptor conductances. Here, by utilizing specific visual stimuli and single optogenetic probe pulses, we show that the function of ​parvalbumin-expressing and ​somatostatin-expressing neurons in mice in vivo is governed by the overlap of response timing between these neurons and their targets. In particular, ​somatostatin-expressing neurons respond at longer latencies to small visual stimuli compared with their target neurons and provide subtractive inhibition. With large visual stimuli, however, they respond at short latencies coincident with their target cells and switch to provide divisive inhibition. These results indicate that inhibition mediated by these neurons is a dynamic property of cortical circuits rather than an immutable property of neuronal classes.Marie Curie International Fellowship (Postdoctoral Fellowship FP7-PEOPLE-2010-IOF))National Institutes of Health (U.S.) (Grant EY007023)National Institutes of Health (U.S.) (Grant NS090473)Simons Foundatio

Equilibrium correlations in charged fluids coupled to the radiation field

We provide an exact microscopic statistical treatment of particle and field correlations in a system of quantum charges in equilibrium with a classical radiation field. Using the Feynman-Kac-Ito representation of the Gibbs weight, the system of particles is mapped onto a collection of random charged wires. The field degrees of freedom can be integrated out, providing an effective pairwise magnetic potential. We then calculate the contribution of the transverse field coupling to the large-distance particle correlations. The asymptotics of the field correlations in the plasma are also exactly determined.Comment: 31 pages, 0 figures. PACS 05.30.-d, 05.40.-a, 11.10.Wx. Changes: Improved comparison with existing literature on field correlations. Added Concluding Remarks. References update

Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI

Sami El-Boustani, professeur assistant au Département de Neurosciences Fondamentales de la Faculté de médecine de l’UNIGE © Sami El-Boustani <p>--------</p> Sami El-Boustani, Assistant Professor in the Department of Basic Neurosciences at the Faculty of Medicine of the UNIGE © Sami El-Boustani

Sami El-Boustani, professeur assistant au Département de Neurosciences Fondamentales de la Faculté de médecine de l’UNIGE © Sami El-Boustani -------- Sami El-Boustani, Assistant Professor in the Department of Basic Neurosciences at the Faculty of Medicine of the UNIGE © Sami El-Boustan

Apprentissage et codage de corrélation dans des réseaux d'état stochastique

L'activité des réseaux de neurones corticaux est caractérisée par des motifs de décharge stochastiques. Cependant, la nature exacte de ces états, et comment un apprentissage stable et un codage d'information peuvent se produire dans de tels états, restent mal compris. Nous avons d'abord étudié des modèles de réseaux de neurones capables de reproduire les régimes d'activité observés in vivo. Ces régimes asynchrones et irréguliers sont modélisés à l'aide d une description Markovienne en utilisant une équation maîtresse phénoménologique. Afin de disséquer les corrélations présentes dans l'activité corticale, nous avons développé différents outils d'analyse. Nous trouvons que la réponse sous-liminaire de neurones dans V1 peut refléter les corrélations dans le stimulus visuel. Au niveau extracellulaire, nous avons développé un modèle d'Ising qui peut prédire précisément le taux d'occurrence des motifs spatio-temporels de décharge de plusieurs neurones enregistrés simultanément. Nous nous sommes ensuite intéressés à la question du codage de corrélations in vivo. Des enregistrements extracellulaires ont été effectués dans le cortex à tonneaux du rat anesthésié. Nous avons trouvé que le niveau de corrélation affecte les propriétés intégratives des neurones enregistrés. Un modèle fonctionnel suggère que ce résultat peut s'expliquer par des interactions entre cellules de sélectivité opposée. Finalement, nous avons proposé un modèle basé sur la règle de plasticité STDP qui peut stabiliser l'apprentissage dans l'activité spontanée. Ce modèle est équivalent à une règle BCM et peut reproduire des résultats dans l'hippocampe où la méta-plasticité a été observée pour la première fois.PARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF