Victor B. Kazantsev

Spatiotemporal calcium dynamics in single astrocytes and its modulation by neuronal activity

Astrocytes produce a complex repertoire of Ca2+ events that coordinate their major functions. The principle of Ca2+ events integration in astrocytes, however, is unknown. Here we analyze whole Ca2+ events, which were defined as spatiotemporally interconnected transient Ca2+ increases. Using such analysis in single hippocampal astrocytes in culture and in slices we found that spreads and durations of Ca2+ events follow power law distributions, a fingerprint of scale-free systems. A mathematical model demonstrated that such Ca2+ dynamics can arise from intracellular inositol-3-phosphate diffusion. The power law exponent (α) was decreased by activation of metabotropic glutamate receptors (mGluRs) either by specific receptor agonist or by low frequency stimulation of glutamatergic fibers in hippocampal slices. Decrease in α indicated an increase in proportion of large Ca2+ events. Notably, mGluRs activation did not increase the frequency of whole Ca2+ events. This result suggests that neuronal activity does not trigger new Ca2+ events in astrocytes (detectable by our methods), but modulates the properties of existing ones. Thus, our results provide a new perspective on how astrocyte responds to neuronal activity by changing its Ca2+ dynamics, which might further affect local network by triggering release of gliotransmitters and by modulating local blood flow.

Bi-directional astrocytic regulation of neuronal activity within a network

The concept of a tripartite synapse holds that astrocytes can affect both the pre- and post-synaptic compartments through the Ca2+-dependent release of gliotransmitters. Because astrocytic Ca2+ transients usually last for a few seconds, we assumed that astrocytic regulation of synaptic transmission may also occur on the scale of seconds. Here, we considered the basic physiological functions of tripartite synapses and investigated astrocytic regulation at the level of neural network activity. The firing dynamics of individual neurons in a spontaneous firing network was described by the Hodgkin–Huxley model. The neurons received excitatory synaptic input driven by the Poisson spike train with variable frequency. The mean field concentration of the released neurotransmitter was used to describe the presynaptic dynamics. The amplitudes of the excitatory postsynaptic currents (PSCs) obeyed the gamma distribution law. In our model, astrocytes depressed the presynaptic release and enhanced the PSCs. As a result, low frequency synaptic input was suppressed while high frequency input was amplified. The analysis of the neuron spiking frequency as an indicator of network activity revealed that tripartite synaptic transmission dramatically changed the local network operation compared to bipartite synapses. Specifically, the astrocytes supported homeostatic regulation of the network activity by increasing or decreasing firing of the neurons. Thus, the astrocyte activation may modulate a transition of neural network into bistable regime of activity with two stable firing levels and spontaneous transitions between them.

A homeostatic model of neuronal firing governed by feedback signals from the extracellular matrix.

Molecules of the extracellular matrix (ECM) can modulate the efficacy of synaptic transmission and neuronal excitability. These mechanisms are crucial for the homeostatic regulation of neuronal firing over extended timescales. In this study, we introduce a simple mathematical model of neuronal spiking balanced by the influence of the ECM. We consider a neuron receiving random synaptic input in the form of Poisson spike trains and the ECM, which is modeled by a phenomenological variable involved in two feedback mechanisms. One feedback mechanism scales the values of the input synaptic conductance to compensate for changes in firing rate. The second feedback accounts for slow fluctuations of the excitation threshold and depends on the ECM concentration. We show that the ECM-mediated feedback acts as a robust mechanism to provide a homeostatic adjustment of the average firing rate. Interestingly, the activation of feedback mechanisms may lead to a bistability in which two different stable levels of average firing rates can coexist in a spiking network. We discuss the mechanisms of the bistability and how they may be related to memory function.

A Spiking Neural Network in sEMG Feature Extraction.

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control.

Myoelectric Control System of Lower Limb Exoskeleton for Re-training Motion Deficiencies

Technical progress in robotics has led to its expansion into different spheres of human life. Rehabilitation medicine has become one of those areas, actively adopting robotic principles and achievements. Over the last decade the progress in this area is inseparably linked with the development of robotic devices - exoskeletons designed to compensate lower limb disability arising as a result of brain or spinal cord injuries and diseases. In this paper, we propose a new control approach for robotic exoskeleton which integrates muscle bioelectric signals of the patient in the control loop. We believe that the proposed approach activates biofeedback mechanisms, which in turn intensify the rehabilitation process.

Adaptive and Phase Selective Spike Timing Dependent Plasticity in Synaptically Coupled Neuronal Oscillators

We consider and analyze the influence of spike-timing dependent plasticity (STDP) on homeostatic states in synaptically coupled neuronal oscillators. In contrast to conventional models of STDP in which spike-timing affects weights of synaptic connections, we consider a model of STDP in which the time lags between pre- and/or post-synaptic spikes change internal state of pre- and/or post-synaptic neurons respectively. The analysis reveals that STDP processes of this type, modeled by a single ordinary differential equation, may ensure efficient, yet coarse, phase-locking of spikes in the system to a given reference phase. Precision of the phase locking, i.e. the amplitude of relative phase deviations from the reference, depends on the values of natural frequencies of oscillators and, additionally, on parameters of the STDP law. These deviations can be optimized by appropriate tuning of gains (i.e. sensitivity to spike-timing mismatches) of the STDP mechanism. However, as we demonstrate, such deviations can not be made arbitrarily small neither by mere tuning of STDP gains nor by adjusting synaptic weights. Thus if accurate phase-locking in the system is required then an additional tuning mechanism is generally needed. We found that adding a very simple adaptation dynamics in the form of slow fluctuations of the base line in the STDP mechanism enables accurate phase tuning in the system with arbitrary high precision. Adaptation operating at a slow time scale may be associated with extracellular matter such as matrix and glia. Thus the findings may suggest a possible role of the latter in regulating synaptic transmission in neuronal circuits.

Synaptic multistability and network synchronization induced by the neuron–glial interaction in the brain

The dynamics of a synaptic contact between neurons that forms a feedback loop through the interaction with glial cells of the brain surrounding the neurons is studied. It is shown that, depending on the character of the neuron–glial interaction, the dynamics of the signal transmission frequency in the synaptic contact can be bistable with two stable steady states or spiking with the regular generation of spikes with various amplitudes and durations. It is found that such a synaptic contact at the network level is responsible for the appearance of quasisynchronous network bursts.

Model of self-oscillations in a neuron generator under the action of an active medium

Mechanisms of generation of self-oscillations in a mathematical model of a neuron embedded in an active medium have been analyzed. The processes of generation of pulsed electric activity are mediated by ionic currents through the cell membrane of the neuron, which can in turn be controlled by neuroactive substances distributed in the extracellular space. Bifurcation mechanisms of the generation and destruction of self-oscillations, as well as their characteristics, have been studied within a neuron generator model developed on the basis of neurobiological experiments.

Theta rhythm-like bidirectional cycling dynamics of living neuronal networks in vitro

The phenomena of synchronization, rhythmogenesis and coherence observed in brain networks are believed to be a dynamic substrate for cognitive functions such as learning and memory. However, researchers are still debating whether the rhythmic activity emerges from the network morphology that developed during neurogenesis or as a result of neuronal dynamics achieved under certain conditions. In the present study, we observed self-organized spiking activity that converged to long, complex and rhythmically repeated superbursts in neural networks formed by mature hippocampal cultures with a high cellular density. The superburst lasted for tens of seconds and consisted of hundreds of short (50–100 ms) small bursts with a high spiking rate of 139.0 ± 78.6 Hz that is associated with high-frequency oscillations in the hippocampus. In turn, the bursting frequency represents a theta rhythm (11.2 ± 1.5 Hz). The distribution of spikes within the bursts was non-random, representing a set of well-defined spatio-temporal base patterns or motifs. The long superburst was classified into two types. Each type was associated with a unique direction of spike propagation and, hence, was encoded by a binary sequence with random switching between the two “functional” states. The precisely structured bidirectional rhythmic activity that developed in self-organizing cultured networks was quite similar to the activity observed in the in vivo experiments.

Glial cell line-derived neurotrophic factor (GDNF) counteracts hypoxic damage to hippocampal neural network function in vitro

Glial cell line-derived neurotrophic factor (GDNF) is regarded as a potent neuroprotector and a corrector of neural network activity in stress conditions. This work aimed to investigate the effect of GDNF on primary hippocampal cultures during acute normobaric hypoxia. Hypoxia induction was performed using day 14 in vitro cultures derived from mouse embryos (E18) with the preventive addition of GDNF (1 ng/ml) to the culture medium 10 min before oxygen deprivation. An analysis of spontaneous bioelectrical activity that included defining the internal neural network structure, morphological studies, and viability tests was performed during the post-hypoxic period. This study revealed that GDNF does not influence spontaneous network activity during normoxia but protects cultures from cell death and maintains the network activity during hypoxia. GDNF created unique conditions that supported the viability of cells even in cases of cellular mitochondrial damage. GDNF partially negated the consequences of hypoxia by influencing synaptic plasticity. Intravital mRNA detection identified fewer GluR2 mRNA-positive cells, whereas GDNF preserved the number of these cells in the post-hypoxic period. Activation of the synthesis of GluR2 subunits of AMPA-receptors is one possible mechanism of the neuroprotective action of GDNF.