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The article presents four branches of multi-year research of human breathing. They are (1) modeling and study the movement of the convective flows in the artificial models of nose; (2) numerical modeling of the convective flows movement on the computer models; (3) breathing research with the help of diagnostic equipment; (4) determination of the exhaled air composition. In all our studies the breathing was examined as a dynamical process. The results of pressure, temperature, velocity and concentration CO2 measuring in the nostrils and in the significant parts of the artificial model of nasal cavity are presented. For this measurements estimated the correlation entropy, correlation dimension. The 3D geometrical model of human nasal cavities obtained from computer-aided tomography data using Mercury Amira program is also given. The 3D unstructured mesh with 1,5×107 finite elements was constructed after the segmentation using Altair Hypermesh software had been finished. The mesh was used to set up an unsteady simulation of airflow inside the obtained geometrical model. Application of DES method on the mesh of a good quality made it possible to distinguish the small-scale turbulent swirls inside the flow. A gas sensor based on spontaneous Raman scattering is proposed for the compositional analysis of single breath events. The Raman sensor is able to detect all the major gas components, i.e. N2, O2, CO2, and H2O at ambient pressure with a high temporal resolution. Concentration fluctuations within a single breath event could be resolved.

Short-term synaptic plasticity (STP) critically affects the processing of information in neuronal circuits by reversibly changing the effective strength of connections between neurons on time scales from milliseconds to a few seconds. STP is traditionally studied using intracellular recordings of postsynaptic potentials or currents evoked by presynaptic spikes. However, STP also affects the statistics of postsynaptic spikes. Here we present two model-based approaches for estimating synaptic weights and short-term plasticity from pre- and postsynaptic spike observations alone. We extend a generalized linear model (GLM) that predicts postsynaptic spiking as a function of the observed pre- and postsynaptic spikes and allow the connection strength (coupling term in the GLM) to vary as a function of time based on the history of presynaptic spikes. Our first model assumes that STP follows a Tsodyks-Markram description of vesicle depletion and recovery. In a second model, we introduce a functional description of STP where we estimate the coupling term as a biophysically unrestrained function of the presynaptic inter-spike intervals. To validate the models, we test the accuracy of STP estimation using the spiking of pre- and postsynaptic neurons with known synaptic dynamics. We first test our models using the responses of layer 2/3 pyramidal neurons to simulated presynaptic input with different types of STP, and then use simulated spike trains to examine the effects of spike-frequency adaptation, stochastic vesicle release, spike sorting errors, and common input. We find that, using only spike observations, both model-based methods can accurately reconstruct the time-varying synaptic weights of presynaptic inputs for different types of STP. Our models also capture the differences in postsynaptic spike responses to presynaptic spikes following short vs long inter-spike intervals, similar to results reported for thalamocortical connections. These models may thus be useful tools for characterizing short-term plasticity from multi-electrode spike recordings in vivo.

Mice deficient in the extracellular matrix glycoprotein tenascin-C (TNC−/−) express a deficit in specific forms of hippocampal synaptic plasticity, which involve the L-type voltage-gated Ca2+ channels (L-VGCCs). The mechanisms underlying this deficit and its functional implications for learning and memory have not been investigated. In line with previous findings, we report on impairment in theta-burst stimulation (TBS)-induced long-term potentiation (LTP) in TNC−/− mice in the CA1 hippocampal region and its rescue by the L-VGCC activator Bay K-8644. We further found that the overall pattern of L-VGCC expression in the hippocampus in TNC−/− mice was normal, but Western blot analysis results uncovered upregulated expression of the Cav1.2 and Cav1.3 α-subunits of L-VGCCs. However, these L-VGCCs were not fully functional in TNC−/− mice, as demonstrated by Ca2+ imaging, which revealed a reduction of nifedipine-sensitive Ca2+ transients in CA1 pyramidal neurons. TNC−/− mice showed normal learning and memory in the contextual fear conditioning paradigm but impaired extinction of conditioned fear responses. Systemic injection of the L-VGCC blockers nifedipine and diltiazem into wild-type mice mimicked the impairment of fear extinction observed in TNC−/− mice. The deficiency in TNC−/− mice substantially occluded the effects of these drugs. Our results suggest that TNC-mediated modulation of L-VGCC activity is essential for fear extinction.

Understanding single-neuron computations and encoding performed by spike-generation mechanisms of cortical neurons is one of the central challenges for cell electrophysiology and computational neuroscience. An established paradigm to study spike encoding in controlled conditions in vitro uses intracellular injection of a mixture of signals with fluctuating currents that mimic in vivo-like background activity. However this technique has two serious limitations: it uses current injection, while synaptic activation leads to changes of conductance, and current injection is technically most feasible in the soma, while the vast majority of synaptic inputs are located on the dendrites. Recent progress in optogenetics provides an opportunity to circumvent these limitations. Transgenic expression of light-activated ionic channels, such as Channelrhodopsin2 (ChR2), allows induction of controlled conductance changes even in thin distant dendrites. Here we show that photostimulation provides a useful extension of the tools to study neuronal encoding, but it has its own limitations. Optically induced fluctuating currents have a low cutoff (~70 Hz), thus limiting the dynamic range of frequency response of cortical neurons. This leads to severe underestimation of the ability of neurons to phase-lock their firing to high frequency components of the input. This limitation could be worked around by using short (2 ms) light stimuli which produce membrane potential responses resembling EPSPs by their fast onset and prolonged decay kinetics. We show that combining application of short light stimuli to different parts of dendritic tree for mimicking distant EPSCs with somatic injection of fluctuating current that mimics fluctuations of membrane potential in vivo, allowed us to study fast encoding of artificial EPSPs photoinduced at different distances from the soma. We conclude that dendritic photostimulation of ChR2 with short light pulses provides a powerful tool to investigate population encoding of simulated synaptic potentials generated in dendrites at different distances from the soma.

The generation of action potentials (APs) is a key process in the operation of nerve cells and the communication between neurons. Action potentials in mammalian central neurons are characterized by an exceptionally fast onset dynamics, which differs from the typically slow and gradual onset dynamics seen in identified snail neurons. Here we describe a novel method of analysis which provides a quantitative measure of the onset dynamics of action potentials. This method captures the difference between the fast, step-like onset of APs in rat neocortical neurons and the gradual, exponential-like AP onset in identified snail neurons. The quantitative measure of the AP onset dynamics, provided by the method, allows us to perform quantitative analyses of factors influencing the dynamics.