Assemblages de cellules dans enregistrements neuronaux : identification et étude par l’inférence de modèles de réseaux fonctionnels et techniques de physique statistique

This thesis illustrates a research on cell assemblies, groups of closely connected, synchronously activating neurons, which are thought to be the units of memory. After a review of the main experimental and theoretical advances in this field, and of the techniques of statistical physics and inference for the study of interacting neurons, a new method to unveil cell assemblies from neuronal data is illustrated and applied to multi-electrode recordings in the prefrontal cortex of rats during performance of a task and during the preceding and following sleep epochs. The method is based on the inference of an Ising network of effective interactions between the neurons and on the simulation of the inferred model in the presence of a global uniform drive: as the drive increases, configurations of high activity (cell assemblies) are unveiled, which activate in the data on time scales of tens of ms, in the presence of transient stimuli. The assemblies are robust with respect to noise. Comparisonof the interaction networks and of the results of the simulations across the three experimental phases reveals empirical rules for the modification of cell assemblies. The inferred model is also exploited to estimate the reactivation (replay) of the cell assemblies during sleep, important for memory consolidation. Inference and sampling of a generalized linear model show that there is not a specific order of activation of the neurons in the groups. It is finally discussed an application of descriptive statistics to the study of synaptic plasticity of neurons in vitro in an optogenetic framework.Cette thèse illustre une recherche sur les assemblées de cellules, groupes de neurones étroitement liés et co-activés, considérés comme les unités de la mémoire. Après une revue des majeures avancées expérimentales et théoriques dans ce domaine, et des techniques de physique statistique et d'inférence pour l'étude de neurones en interaction, on présente une nouvelle méthode pour dévoiler les assemblées decellules à partir des données neuronales et on montre son application à des enregistrements multi-électrodes dans le cortex préfrontal de rats pendant l'exécution d'une tâche et les époques de sommeil précédant et suivant. La méthode est basée sur l'inférence d'un réseau d'Ising d’interactions effectives entre les neurones et sur la simulation du modèle inféré en présence d'une entrée globale uniforme: quand l'entrée augmente, on découvre des configurations d'activité élevée (assemblées de cellules), qui s'activent dans les données à des échelles de temps de dizaines de ms en présence de stimuli transitoires. Les assemblées sont robustes par rapport au bruit. La comparaison des réseaux d'interactions et des résultats des simulations à travers les trois phases expérimentales révèle des règles empiriques pour la modification des assemblées de cellules. Le modèle inféré est également exploité pour estimer la réactivation (replay) des assemblées pendant le sommeil, important pour la consolidation de la mémoire. Inférence et échantillonnage d'un modèle linéaire généralisé montrent qu'il n'y a pas un ordre d'activation spécifique des neurones. On discute enfin une application de statistique descriptive à l'étude de la plasticité synaptique in vitro dans un cadre optogénétique

Cortical feedback and gating in odor discrimination and generalization

A central question in neuroscience is how context changes perception. In the olfactory system, for example, experiments show that task demands can drive divergence and convergence of cortical odor responses, likely underpinning olfactory discrimination and generalization. Here, we propose a simple statistical mechanism for this effect based on unstructured feedback from the central brain to the olfactory bulb, which represents the context associated with an odor, and sufficiently selective cortical gating of sensory inputs. Strikingly, the model predicts that both convergence and divergence of cortical odor patterns should increase when odors are initially more similar, an effect reported in recent experiments. The theory in turn predicts reversals of these trends following experimental manipulations and in neurological conditions that increase cortical excitability

Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity

Contains fulltext : 197916.pdf (publisher's version ) (Open Access

Correction: A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems.

[This corrects the article DOI: 10.1371/journal.pcbi.1003150.]

Correction: A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems - Fig 6

<p>Examples comparing estimates and run-length distributions from the full Bayesian model and our reduced approximation for the cases of Bernoulli data (A, D, G), Gaussian data with unknown mean (B, E, H), and Gaussian data with a constant mean but unknown variance (C, F, I). (A, B, C) input data (grey), model estimates (blue: full model; red: reduced model), and the ground truth generative parameter (mean for A and B, standard deviation in C; dashed black line). Run-length distributions computed for the full model (D, E, F) and reduced model (G, H, I) are shown for each of the examples.</p

Error (normalized by the variance of the prior, E2 0) computed from simulations as a function of hazard rate for the reduced model at the optimal parameter settings as shown in _gure 8.

<p>Gaussian case with 1 (left), 2 (center), or 3 (right) nodes.</p

Correction: A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems - Fig 3

<p>Schematic of the message passing algorithm for the full (A) and approximate (B) algorithms. For the approximate algorithm we only show the case for li+1 li + 1.</p