Gamma Oscillations in a Nonlinear Regime: A Minimal Model Approach Using Heterogeneous Integrate-and-Fire Networks

Fast oscillations and in particular gamma-band oscillation (20-80 Hz) are commonly observed during brain function and are at the center of several neural processing theories. In many cases, mathematical analysis of fast oscillations in neural networks has been focused on the transition between irregular and oscillatory firing viewed as an instability of the asynchronous activity. But in fact, brain slice experiments as well as detailed simulations of biological neural networks have produced a large corpus of results concerning the properties of fully developed oscillations that are far from this transition point. We propose here a mathematical approach to deal with nonlinear oscillations in a network of heterogeneous or noisy integrate-and-fire neurons connected by strong inhibition. This approach involves limited mathematical complexity and gives a good sense of the oscillation mechanism, making it an interesting tool to understand fast rhythmic activity in simulated or biological neural networks. A surprising result of our approach is that under some conditions, a change of the strength of inhibition only weakly influences the period of the oscillation. This is in contrast to standard theoretical and experimental models of interneuron network gamma oscillations (ING), where frequency tightly depends on inhibition strength, but it is similar to observations made in some in vitro preparations in the hippocampus and the olfactory bulb and in some detailed network models. This result is explained by the phenomenon of suppression that is known to occur in strongly coupled oscillating inhibitory networks but had not yet been related to the behavior of oscillation frequency

Spike-timing prediction in cortical neurons with active dendrites

A complete single-neuron model must correctly reproduce the firing of spikes and bursts. We present a study of a simplified model of deep pyramidal cells of the cortex with active dendrites. We hypothesized that we can model the soma and its apical dendrite with only two compartments, without significant loss in the accuracy of spike-timing predictions. The model is based on experimentally measurable impulse-response functions, which transfer the effect of current injected in one compartment to current reaching the other. Each compartment was modeled with a pair of non-linear differential equations and a small number of parameters that approximate the Hodgkin-and-Huxley equations. The predictive power of this model was tested on electrophysiological experiments where noisy current was injected in both the soma and the apical dendrite simultaneously. We conclude that a simple two-compartment model can predict spike times of pyramidal cells stimulated in the soma and dendrites simultaneously. Our results support that regenerating activity in the apical dendritic is required to properly account for the dynamics of layer 5 pyramidal cells under in-vivo-like conditions

Analysis of information processing in the olfactory bulb by in vivo experiments and theoretical modelling

Much information is conveyed to animals by the diverse molecules propagated in their environment. This information is transmitted and treated within the olfactory system to be utilized by the rest of the brain. In this process, the sensory flow arriving from the nose first encounters the olfactory bulb. This thesis studies different aspects of the encoding and treatment of olfactory information in the bulb by a combination of experimental and theoretical approaches. The olfactory bulb consists of two functional stages: an input stage where the nerve terminals from the sensory neurons of the nose are spatially arranged in a precise manner, and an output stage corresponding to the neurons which propagate the processed information towards the more central brain areas. The first part of this thesis is focused on the development of an image processing technique to precisely map the input stage. In a second part, output neurons activity in responsive areas of the bulb was recorded in anaesthetized mice. The data obtained indicate complex dynamics in neural activity on several time scales. We show that the combination of mean firing rates from a large enough ensemble of neurons allows to accurately predict the odor presented to the animal. We also show that temporal variables related to the dynamics give very little information complementary to the ensemble rate code, suggesting that these variables are not read by brain structures downstream to the bulb. In a third part, we developed a numerical model of the olfactory bulb to understand its fast oscillatory dynamics. The model proposes a mechanism based on the negative feedback from the interneurons that regulate output neurons. Some predictions of the model are checked experimentally. An analytical interpretation of the model is also proposed and its generalization to the analysis of fast oscillation in other parts of the brain is discussed

Neural encoding of sensory and behavioral complexity in the auditory cortex

International audienceConverging evidence now supports the idea that auditory cortex is an important step for the emergence of auditory percepts. Recent studies have extended the list of complex, nonlinear sound features coded by cortical neurons. Moreover, we are beginning to uncover general properties of cortical representations, such as invariance and discreteness, which reflect the structure of auditory perception. Complexity, however, emerges not only through nonlinear shaping of auditory information into perceptual bricks. Behavioral context and task-related information strongly influence cortical encoding of sounds via ascending neuromodulation and descending top-down frontal control. These effects appear to be mediated through local inhibitory networks. Thus, auditory cortex can be seen as a hub linking structured sensory representations with behavioral variables

Protocol for implementing medial forebrain bundle stimulation as a reward for perceptual tasks in mice

International audienceTraining mice to perform perceptual tasks is a vital part of integrative neuroscience. Replacing classical rewards like water with medial forebrain bundle (MFB) stimulation allows experimenters to avoid deprivation and obtain higher trial numbers per session. Here, we provide a protocol for implementing MFB-based reward in mice. We describe steps for MFB electrode implantation, efficacy testing, and stimulation calibration. After these steps, MFB reward can be used to facilitate sensory discrimination task training and enable nuanced characterization of psychophysical abilities

Fast cortical dynamics encode tactile grating orientation during active touch

International audienceTouch-based object recognition relies on perception of compositional tactile features like roughness, shape, and surface orientation. However, besides roughness, it remains unclear how these different tactile features are encoded by neural activity that is linked with perception. Here, we establish a cortex-dependent perceptual task in which mice discriminate tactile gratings on the basis of orientation using only their whiskers. Multielectrode recordings in the barrel cortex reveal weak orientation tuning in average firing rates (500-ms time scale) during grating exploration despite high levels of cortical activity. Just before decision, orientation information extracted from fast cortical dynamics (100-ms time scale) more closely resembles concurrent psychophysical measurements than single neuron orientation tuning curves. This temporal code conveys both stimulus and choice/action-related information, suggesting that fast cortical dynamics during exploration of a tactile object both reflect the physical stimulus and affect the decision

Gamma oscillations in a nonlinear regime: a minimal model approach using heterogeneous integrate-and-fire networks

Fast oscillations and in particular gamma-band oscillation (20-80 Hz) are commonly observed during brain function and are at the center of several neural processing theories. In many cases, mathematical analysis of fast oscillations in neural networks has been focused on the transition between irregular and oscillatory firing viewed as an instability of the asynchronous activity. But in fact, brain slice experiments as well as detailed simulations of biological neural networks have produced a large corpus of results concerning the properties of fully developed oscillations that are far from this transition point. We propose here a mathematical approach to deal with nonlinear oscillations in a network of heterogeneous or noisy integrate-and-fire neurons connected by strong inhibition. This approach involves limited mathematical complexity and gives a good sense of the oscillation mechanism, making it an interesting tool to understand fast rhythmic activity in simulated or biological neural networks. A surprising result of our approach is that under some conditions, a change of the strength of inhibition only weakly influences the period of the oscillation. This is in contrast to standard theoretical and experimental models of interneuron network gamma oscillations (ING), where frequency tightly depends on inhibition strength, but it is similar to observations made in some in vitro preparations in the hippocampus and the olfactory bulb and in some detailed network models. This result is explained by the phenomenon of suppression that is known to occur in strongly coupled oscillating inhibitory networks but had not yet been related to the behavior of oscillation frequency