arXiv subjects

Denis Zakharov

Publications and source records attributed to Denis Zakharov.

7 recordsLinked to original sources

arXiv2026

Hypergraphs from multivariate connectivity: caCOH-based EEG/MEG representation

Hypergraphs provide a natural framework for representing neurophysiological interactions distributed across sets of sensors. A key methodological question is how hyperedges should be defined from frequency-resolved electroencephalography/magnetoencephalography (EEG/MEG) data. We demonstrate a construction strategy in which hyperedges are obtained from canonical coherence (caCOH), an extension of coherence that estimates coupling between multidimensional signal spaces. To our knowledge, this is the first work to construct hypergraphs directly from a multivariate connectivity measure specifically designed for frequency-resolved neurophysiological analysis. We propose two caCOH-based representations: a one-to-space hypergraph, where each external signal defines a hyperedge over the EEG/MEG sensor space, and a space-to-space hypergraph, where two multidimensional signal spaces are represented by a single hyperedge. We evaluate the approach in controlled simulations with known coupling frequencies and varying signal-to-noise ratio (SNR). Compared with graphs based on magnitude-squared coherence (MSC), caCOH-based hypergraphs showed statistically higher target-baseline contrasts at almost all SNR levels, indicating stronger recovery of coupling frequencies. They also recovered sensor-level spatial patterns associated with the simulated sources. In addition, one-to-space and space-to-space representations reduced 610 MSC edges per frequency to 10 and 1 hyperedges, respectively. These results establish multivariate spectral connectivity as a natural methodological basis for EEG/MEG hypergraphs.

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arXiv2026

Ensemble-based graph representation of fMRI data for cognitive brain state classification

fMRI is a non-invasive technique for investigating brain activity, offering high-resolution insights into neural processes. Understanding and decoding cognitive brain states from fMRI depends on how functional interactions are represented. We propose an ensemble-based graph representation in which each edge weight encodes state evidence as the difference between posterior probabilities of two states, estimated by an ensemble of edge-wise probabilistic classifiers from simple pairwise time-series features. We evaluate the method on seven task-fMRI paradigms from the Human Connectome Project, performing binary classification within each paradigm. Using compact node summaries (mean incident edge weights) and logistic regression, we obtain average accuracies of 97.07-99.74 %. We further compare ensemble graphs with conventional correlation graphs using the same graph neural network classifier; ensemble graphs consistently yield higher accuracy (88.00-99.42 % vs 61.86-97.94 % across tasks). Because edge weights have a probabilistic, state-oriented interpretation, the representation supports connection- and region-level interpretability and can be extended to multiclass decoding, regression, other neuroimaging modalities, and clinical classification.

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arXiv2025

Metric Framework of Coherent Activity Patterns Identification in Spiking Neuronal Networks

Partial synchronization plays a crucial role in the functioning of neuronal networks: selective, coordinated activation of neurons enables information processing that flexibly adapts to a changing computational context. Since the structure of coherent activity patterns reflects the network's current state, developing automated tools to identify them is a key challenge in neurodynamics. Existing methods for analyzing neuronal dynamics tend to focus on global characteristics of the network, such as its aggregated synchrony level. While this approach can distinguish between the network's main dynamical states, it cannot reveal the localization or properties of distinct coherent patterns. In this work, we propose a new perspective on neural dynamics analysis that enables the study of network coherence at the single-neuron scale. We interpret the network as a metric space of neurons and represent its instantaneous state as an activity function on that space. We identify specific coherent activity clusters as regions where the activity function exhibits spatial continuity. Each cluster's activity is further characterized using the analytical properties of the activity function within that region. This approach yields a concise yet detailed algorithmic profile of the network's activity patterns.

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arXiv2025

Neuro-oscillatory models of cortical speech processing

In this review, we examine computational models that explore the role of neural oscillations in speech perception, spanning from early auditory processing to higher cognitive stages. We focus on models that use rhythmic brain activities, such as gamma, theta, and delta oscillations, to encode phonemes, segment speech into syllables and words, and integrate linguistic elements to infer meaning. We analyze the mechanisms underlying these models, their biological plausibility, and their potential applications in processing and understanding speech in real time, a computational feature that is achieved by the human brain but not yet implemented in speech recognition models. Real-time processing enables dynamic adaptation to incoming speech, allowing systems to handle the rapid and continuous flow of auditory information required for effective communication, interactive applications, and accurate speech recognition in a variety of real-world settings. While significant progress has been made in modeling the neural basis of speech perception, challenges remain, particularly in accounting for the complexity of semantic processing and the integration of contextual influences. Moreover, the high computational demands of biologically realistic models pose practical difficulties for their implementation and analysis. Despite these limitations, these models provide valuable insights into the neural mechanisms of speech perception. We conclude by identifying current limitations, proposing future research directions, and suggesting how these models can be further developed to achieve a more comprehensive understanding of speech processing in the human brain.

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arXiv2023

Dynamical manifold dimensionality as characterization measure of chimera states in bursting neuronal networks

Methods that distinguish dynamical regimes in networks of active elements make it possible to design the dynamics of models of realistic networks. A particularly salient example is partial synchronization, which may play a pivotal role in elucidating the dynamics of biological neural networks. Such emergent partial synchronization in structurally homogeneous networks is commonly denoted as chimera states. While several methods for detecting chimeras in networks of spiking neurons have been proposed, these are less effective when applied to networks of bursting neurons. Here we introduce the correlation dimension as a novel approach to identifying dynamic network states. To assess the viability of this new method, we study a network of intrinsically Hindmarsh-Rose neurons with non-local connections. In comparison to other measures of chimera states, the correlation dimension effectively characterizes chimeras in burst neurons, whether the incoherence arises in spikes or bursts. The generality of dimensionality measures inherent in the correlation dimension renders this approach applicable to any dynamic system, facilitating the comparison of simulated and experimental data. We anticipate that this methodology will enable the tuning and simulation of when modelling intricate network processes, contributing to a deeper understanding of neural dynamics.

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arXiv2021

Activity stabilization in a population model of working memory by sinusoidal and noisy inputs

According to mechanistic theories of working memory (WM), information is retained as persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains an open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e. they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the "common-noise" groups compared to the "independent-noise" groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.

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arXiv2021

Robust universal approach to identify travelling chimeras and synchronized clusters in spiking networks

We propose a robust universal approach to identify multiple dynamical states, including stationary and travelling chimera states based on an adaptive coherence measure. Our approach allows automatic disambiguation of synchronized clusters, travelling waves, chimera states, and asynchronous regimes. In addition, our method can determine the number of clusters in the case of cluster synchronization. We further couple our approach with a new speed calculation method for travelling chimeras. We validate our approach by an example of a ring network of type II Morris-Lecar neurons with asymmetrical nonlocal inhibitory connections where we identify a rich repertoire of coherent and wave states. We propose that the method is robust for the networks of phase oscillators and extends to a general class of relaxation oscillator networks.

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