Bulbocortical interplay in olfactory information processing via synchronous oscillations

An oscillator network exhibiting a long-lasting response to an external signal

This study proposes an oscillator network to model the long-lasting responses observed in neural circuits. The responses of the proposed network model are represented by the temporal synchronization of the oscillators. The response duration does not depend on the natural frequency of the oscillators, which allows the responses to last much longer than the oscillation period of the oscillators. We can control the response duration by tuning the connection strengths between the oscillators and the external signal that triggers the responses. It is possible to break and restart the responses regardless of the way in which the oscillators are connected.

Computation in the olfactory system

Computational models are increasingly essential to systems neuroscience. Models serve as proofs of concept, tests of sufficiency, and as quantitative embodiments of working hypotheses and are important tools for understanding and interpreting complex data sets. In the olfactory system, models have played a particularly prominent role in framing contemporary theories and presenting novel hypotheses, a role that will only grow as the complexity and intricacy of experimental data continue to increase. This review will attempt to provide a comprehensive, functional overview of computational ideas in olfaction and outline a computational framework for olfactory processing based on the insights provided by these diverse models and their supporting data.

Disruption of GABA(A) receptors on GABAergic interneurons leads to increased oscillatory power in the olfactory bulb network

Synchronized neural activity is believed to be essential for many CNS functions, including neuronal development, sensory perception, and memory formation. In several brain areas GABA(A) receptor-mediated synaptic inhibition is thought to be important for the generation of synchronous network activity. We have used GABA(A) receptor beta3 subunit deficient mice (beta3-/-) to study the role of GABAergic inhibition in the generation of network oscillations in the olfactory bulb (OB) and to reveal the role of such oscillations in olfaction. The expression of functional GABA(A) receptors was drastically reduced (>93%) in beta3-/- granule cells, the local inhibitory interneurons of the OB. This was revealed by a large reduction of muscimol-evoked whole-cell current and the total current mediated by spontaneous, miniature inhibitory postsynaptic currents (mIPSCs). In beta3-/- mitral/tufted cells (principal cells), there was a two-fold increase in mIPSC amplitudes without any significant change in their kinetics or frequency. In parallel with the altered inhibition, there was a significant increase in the amplitude of theta (80% increase) and gamma (178% increase) frequency oscillations in beta3-/- OBs recorded in vivo from freely moving mice. In odor discrimination tests, we found beta3-/- mice to be initially the same as, but better with experience than beta3+/+ mice in distinguishing closely related monomolecular alcohols. However, beta3-/- mice were initially better and then worse with practice than control mice in distinguishing closely related mixtures of alcohols. Our results indicate that the disruption of GABA(A) receptor-mediated synaptic inhibition of GABAergic interneurons and the augmentation of IPSCs in principal cells result in increased network oscillations in the OB with complex effects on olfactory discrimination, which can be explained by an increase in the size or effective power of oscillating neural cell assemblies among the mitral cells of beta3-/- mice.

Continuum models in neurobiology and information processing

Continuum models have been used with considerable success for single neurons but have been neglected in the study of neuronal populations. In more popular discrete neuronal network models, the geometric details of the neuronal centers are usually neglected. We here give a continuum nonlinear dynamical model and an approximate model which admits the possibility of ascertaining the roles of the various connectivity patterns from center to center in the central nervous system. Frequency transfer characteristics are used to incorporate the nonlinear dynamics of single neurons. Simple examples are evaluated both analytically and numerically and the results presented graphically.

Minimal model of oscillations and waves in the Limax olfactory lobe with tests of the model's predictive power

Propagating waves are observed in the olfactory or procerebral (PC) lobe of the terrestrial mollusk, Limax maximus. Wave propagation is altered by cutting through the various layers of the PC lobe both parallel and transverse to the direction of wave propagation. We present a model for the PC lobe based on two layers of coupled cells. The top layer represents the cell layer of the PC lobe, and the bottom layer corresponds to the neuropil of the PC lobe. To get wave propagation, we induce a coupling gradient so that the most apical cells receive a greater input from neighbors than the basal cells. The top layer in the model is composed of oscillators coupled locally, whereas the bottom layer is comprised of oscillators with global coupling. Odor stimulation is represented by an increase in the strength of coupling between the two layers. This model allows us to explain a number of experimental observations: 1) the intact PC lobe exhibits regular propagating waves, which travel from the apical to the basal end; 2) there is a gradient in the local frequency of slices cut transverse to the axis of wave propagation, with apical slices oscillating faster than basal slices; 3) with partial cuts through the cell layer or the neuropil layer, the apical and basal ends remain tightly coupled; 4) removal of the neuropil layer does not prevent wave propagation in the cell layer; 5) odor stimulation causes the waves to collapse and the cells in the PC lobe oscillate synchronously; and 6) by allowing a single parameter to vary in the model, we capture the reversal of waves in low chloride medium.