laboratoire de physique statistique
laboratoire de physique statistique




Neuronal Morphology Generates High-Frequency Firing Resonance - Ostojic, Srdjan and Szapiro, German and Schwartz, Eric and Barbour, Boris and Brunel, Nicolas and Hakim, Vincent
JOURNAL OF NEUROSCIENCE 357056-7068 (2015)

Abstract : The attenuation of neuronal voltage responses to high-frequency current inputs by the membrane capacitance is believed to limit single-cell bandwidth. However, neuronal populations subject to stochastic fluctuations can follow inputs beyond this limit. We investigated this apparent paradox theoretically and experimentally using Purkinje cells in the cerebellum, a motor structure that benefits from rapid information transfer. We analyzed the modulation of firing in response to the somatic injection of sinusoidal currents. Computational modeling suggested that, instead of decreasing with frequency, modulation amplitude can increase up to high frequencies because of cellular morphology. Electrophysiological measurements in adult rat slices confirmed this prediction and displayed a marked resonance at 200 Hz. We elucidated the underlying mechanism, showing that the two-compartment morphology of the Purkinje cell, interacting with a simple spiking mechanism and dendritic fluctuations, is sufficient to create high-frequency signal amplification. This mechanism, which we term morphology-induced resonance, is selective for somatic inputs, which in the Purkinje cell are exclusively inhibitory. The resonance sensitizes Purkinje cells in the frequency range of population oscillations observed in vivo.
Automated imaging of neuronal activity in freely behaving Caenorhabditis elegans - Ben Arous, Juliette and Tanizawa, Yoshinori and Rabinowitch, Ithai and Chatenay, Didier and Schafer, William R.

Abstract : In order to understand how neuronal circuits control locomotory patterns it is necessary to record neuronal activity of freely behaving animals. Here, using a new automated system for simultaneous recording of behavior and neuronal activity in freely moving Caenorhabditis elegans on standard agar plates, we show that spontaneous reversals from forward to backward locomotion reflect precisely the activity of the AVA command interneurons. We also witness spontaneous activity transients in the PLM sensory neurons during free behavior of the worm in standard conditions. We show that these activity transients are coupled to short spontaneous forward accelerations of the worm. (C) 2010 Elsevier B.V. All rights reserved.
How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains - Ostojic, Srdjan and Brunel, Nicolas and Hakim, Vincent
JOURNAL OF NEUROSCIENCE 2910234-10253 (2009)

Abstract : Functional interactions between neurons in vivo are often quantified by cross-correlation functions (CCFs) between their spike trains. It is therefore essential to understand quantitatively how CCFs are shaped by different factors, such as connectivity, synaptic parameters, and background activity. Here, we study the CCF between two neurons using analytical calculations and numerical simulations. We quantify the role of synaptic parameters, such as peak conductance, decay time, and reversal potential, and analyze how various patterns of connectivity influence CCF shapes. In particular, we find that the symmetry of the CCF distinguishes in general, but not always, the case of shared inputs between two neurons from the case in which they are directly synaptically connected. We systematically examine the influence of background synaptic inputs from the surrounding network that set the baseline firing statistics of the neurons and modulate their response properties. We find that variations in the background noise modify the amplitude of the cross-correlation function as strongly as variations of synaptic strength. In particular, we show that the postsynaptic neuron spiking regularity has a pronounced influence on CCF amplitude. This suggests an efficient and flexible mechanism for modulating functional interactions.
The Statistics of Repeating Patterns of Cortical Activity Can Be Reproduced by a Model Network of Stochastic Binary Neurons - Roxin, Alex and Hakim, Vincent and Brunel, Nicolas
JOURNAL OF NEUROSCIENCE 2810734-10745 (2008)

Abstract : Calcium imaging of the spontaneous activity in cortical slices has revealed repeating spatiotemporal patterns of transitions between so-called down states and up states (Ikegaya et al., 2004). Here we fit a model network of stochastic binary neurons to data from these experiments, and in doing so reproduce the distributions of such patterns. We use two versions of this model: (1) an unconnected network in which neurons are activated as independent Poisson processes; and (2) a network with an interaction matrix, estimated from the data, representing effective interactions between the neurons. The unconnected model (model 1) is sufficient to account for the statistics of repeating patterns in 11 of the 15 datasets studied. Model 2, with interactions between neurons, is required to account for pattern statistics of the remaining four. Three of these four datasets are the ones that contain the largest number of transitions, suggesting that long datasets are in general necessary to render interactions statistically visible. We then study the topology of the matrix of interactions estimated for these four datasets. For three of the four datasets, we find sparse matrices with long-tailed degree distributions and an overrepresentation of certain network motifs. The remaining dataset exhibits a strongly interconnected, spatially localized subgroup of neurons. In all cases, we find that interactions between neurons facilitate the generation of long patterns that do not repeat exactly.