Dense dissociated hippocampal cultures are known to generate spontaneous bursting electrical activity which can be recorded by multielectrode arrays. We have analyzed spatio-temporal profiles of the distribution of spikes in the bursts recorded after 2 weeks in vitro. We have found a statistically significant similarity between the spiking patterns in sequential bursting events, we refer to these spiking patterns as spiking signatures. Such spiking signatures may appear in different parts of the bursts, including the activation patterns – the first spike times in the bursts, and deactivation patterns – the last spike times in the bursts. Moreover, these patterns may display apparent time scaling, e.g., they may be replayed in the subsequent bursts at different speeds, while preserving the spiking order. We discuss how such properties of the bursts may be associated with the formation of repeatable signaling pathways in cultured networks in vitro.

The functional role of astrocyte calcium signaling in brain information processing was intensely debated in recent decades. This interest was motivated by high resolution imaging techniques showing highly developed structure of distal astrocyte processes. Another point was the evidence of bi-directional astrocytic regulation of neuronal activity. To analyze the effects of interplay of calcium signals in processes and in soma mediating correlations between local signals and the cell-level response of the astrocyte we proposed spatially extended model of the astrocyte calcium dynamics. Specifically, we investigated how spatiotemporal properties of Ca 2+ dynamics in spatially extended astrocyte model can coordinate (e.g., synchronize) networks of neurons and synapses.

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.

Discovering the mechanisms underlying homeostatic regulation in brain neural network formation and stability processes is one of the most urgent tasks in modern neuroscience. Brain-derived neurotrophic factor (BDNF) and the tropomyosin-related kinase B (TrkB) receptor system have long been considered the main regulators of neuronal survival and differentiation. The elucidation of methods for studying neural network activity makes investigating the complex mechanisms underlying neural network structure reorganization during development and detecting new mechanisms for neuronal activity remodeling possible. In this in vitro study, we investigated the effects of chronic BDNF (the main TrkB stimulator) and ANA-12 (a TrkB receptor system blocker) administration on the formation of neural-glial networks. The formation of spontaneous bioelectrical activity and functional neural structure depend on TrkB receptors, and blocking TrkB receptors inhibits full bioelectrical activity development. Cross-correlation analysis demonstrated the decisive role of TrkB in the formation and “strengths” of activity centers. Even though an appropriate ANA-12 concentration is non-toxic to nerve cells, numerous cells in culture medium containing this reagent do not exhibit metabolic activity and are not functionally involved in signal transmission processes. Electron microscopy studies revealed that chronically influencing the TrkB receptor system significantly alters synaptic and mitochondrial apparatus capture in cells, and functional analysis of mitochondrial activity confirmed these findings. Because knowledge of interactions between TrkB-mediated regulation and the mitochondrial state under normal conditions is rather limited, data on these relationships are particularly interesting and require further investigation. Thus, we assume that the molecular cascades mediated by TrkB actively participate in the formation of functionally complete brain neural networks.

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.