Olfactory bulb

Summary

The olfactory bulb (Latin: bulbus olfactorius) is a neural structure of the vertebrate forebrain involved in olfaction, the sense of smell. It sends olfactory information to be further processed in the amygdala, the orbitofrontal cortex (OFC) and the hippocampus where it plays a role in emotion, memory and learning. The bulb is divided into two distinct structures: the main olfactory bulb and the accessory olfactory bulb. The main olfactory bulb connects to the amygdala via the piriform cortex of the primary olfactory cortex and directly projects from the main olfactory bulb to specific amygdala areas. The accessory olfactory bulb resides on the dorsal-posterior region of the main olfactory bulb and forms a parallel pathway. Destruction of the olfactory bulb results in ipsilateral anosmia, while irritative lesions of the uncus can result in olfactory and gustatory hallucinations. Structure In most vertebrates, the olfactory bulb is the most rostral (forward) part of the br

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https://en.wikipedia.org/wiki/Olfactory_bulb

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Towards a Canonical Microcircuit of the Olfactory Bulb

Michele Pignatelli

2009

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

Brice Bathellier

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.

EPFL2007

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

Brice Bathellier

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.

Massachusetts Institute of Technology Press2008

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

Brice Bathellier

EPFL2007