Biology subjects

Pajevic, S.

Publications and source records attributed to Pajevic, S..

7 recordsLinked to original sources

bioRxiv2026

Glider compositionality and information capacity in an E/I-balanced neuronal automaton

In cellular automata, localized activity patterns called gliders propagate and interact to carry and transform information. Whether neuronal networks can harness this computational power remains unclear. Here, we demonstrate that a cortical network model with separate excitatory and inhibitory populations, graded cellular activity and uniform local connectivity spontaneously generates diverse, interacting gliders. An analytical reduction identifies the cellular conditions sustaining propagation of the dominant G-glider. Distinct gliders can assemble into growing structures that emit other glider types. Near excitation/inhibition balance, sparse glider dynamics coexist with high computational capacity, measured by input differentiation and generalization, with local capacity varying across space and time. Strong mutual inhibition between interneurons instead permits metastatic proliferation with low computational capacity. These findings connect cellular automata to neuronal information processing and identify interacting gliders as a candidate mechanism for dynamic plasticity: the reorganization of computation within an unchanged synaptic architecture.

neuroscience↗

bioRxiv2026

Critical scaling of cortical ensembles without cooperative amplification

Cortical neurons are sparsely and weakly connected, motivating the long-standing idea that they must act collectively, as cell assemblies, to exert substantial influence on surrounding networks. Yet a few spikes in a single pyramidal neuron can recruit thousands of cortical neurons according to a power law with exponent ~0.285, consistent with a two-dimensional system near a critical percolation transition. Here we holographically stimulated ensembles of 1 to 16 layer 2/3 pyramidal neurons in visual cortex of awake mice while imaging hundreds of neighbouring neurons. Across two orders of magnitude in evoked spike count, network responses followed the same scaling law observed for single-neuron perturbations, with no detectable threshold at which co-activation produced additional amplification. Recruitment was sublinear, suppression increased with ensemble size, and ongoing scale-free avalanche dynamics remained stationary. Nevertheless, ensemble identity became increasingly decodable from surrounding activity. Thus, coordinated firing increases representational specificity without escaping the critical dynamics governing propagation: cortical ensembles convey identity without gaining disproportionate causal impact.

neuroscience↗

bioRxiv2024

Critical Scaling of Novelty in the Cortex

The ability to detect and transmit novel events is essential for adaptive behavior in uncertain environments. Here, we investigate how holographically triggered, unanticipated action potentials propagate through the primary visual cortex of resting mice, focusing on pyramidal neuron communication. We find that these novel spikes -- uncorrelated with ongoing activity -- exert a disproportionately large influence on neighboring neurons, whose response scales as a power law (exponent [~]0.2-0.3). Even a few such spikes can recruit a large fraction of the local network, enabling robust decoding of perturbation origin despite high trial-by-trial variability and ongoing activity dominated by large activity fluctuations in the form of scale-invariant, parabolic neuronal avalanches. Simulations confirm this scaling to small, local perturbations aligns with the high susceptibility of complex systems near criticality. These results suggest that critical dynamics facilitate efficient transmission of novel signals, revealing a fundamental mechanism for cortical novelty detection.

neuroscience↗

bioRxiv2022

Parabolic avalanche scaling in the synchronization of cortical cell assemblies

Neurons in cortex synchronize their spiking in response to local and distant inputs. These synchronized assemblies are fundamental to cortex function, yet basic dynamical aspects about their size and duration are largely unknown. Using 2-photon imaging of neurons in superficial cortex of awake mice, we show that synchronized assemblies organize as scale-invariant avalanches that quadratically grow with duration. This quadratic expansion was found only for correlated neurons and required temporal coarse graining to compensate for spatial subsampling when network dynamics are critical, as demonstrated in simulations. The corresponding time course of an inverted parabola with exponent of {chi} = 2 described avalanches of up to 5 s duration and maximized temporal complexity in the ongoing activity of prefrontal and somatosensory cortex and in visual responses of primary visual cortex. Our results identify a scale-invariant order in the synchronization of highly diverse cortical cell assemblies in the form of parabolic avalanches. Significance StatementThe synchronization of cell assemblies is fundamental to many brain theories. Here we show such synchronization to grow according to an inverted parabola that maximizes temporal complexity. This quadratic scaling found for cell assemblies of highly diverse size and duration is in line with prediction for neuronal avalanches and the cortex being in a critical state.

neuroscience↗

bioRxiv2022

Oligodendrocyte-mediated Myelin Plasticity and its role in Neural Synchronization

Temporal synchrony of signals arriving from different neurons or brain regions is essential for proper neural processing. Nevertheless, it is not well understood how such synchrony is achieved and maintained in a complex network of time-delayed neural interactions. Myelin plasticity has been suggested as an efficient mechanism for controlling timing in brain communications through adaptive changes of axonal conduction velocity and consequently conduction time delays, or latencies; however, local rules and feedback mechanisms that oligodendrocytes (OL) use to achieve synchronization are not known. We propose a mathematical model of oligodendrocyte-mediated myelin plasticity (OMP) in which OL play an active role in providing such feedback. This is achieved without using arrival times at the synapse or modulatory signaling from astrocytes; instead, it relies on the presence of global and transient OL responses to local action potentials in the axons they myelinate. While inspired by OL morphology, we provide the theoretical underpinnings that motivated the model and explore its performance for a wide range of its parameters. Our results indicate that when the characteristic time of OLs spike responses is between 10 and 40 ms and the firing rates in individual axons are relatively low ([lap] 10 Hz), the OMP model efficiently synchronizes correlated and time-locked signals while latencies in axons carrying independent signals are unaffected. This suggests a novel form of selective synchronization in the CNS in which oligodendrocytes play an active role by modulating the conduction delays of correlated spike trains as they traverse to their targets. Significance StatementSynchronization of signals arriving from different neurons or brain regions is of great importance for proper brain function. In vertebrates, myelination provides an efficient way to achieve this by adjusting the conduction velocity and consequently the conduction delays. It is increasingly evident that myelination is an adaptive process and involved in learning; however, the local rules and the feedback that guide oligodendrocytes towards achieving desired delays are not known. Here we propose a simple and biologically plausible model of myelin plasticity in which oligodendrocytes play an active role in providing the needed feedback. We use theoretical arguments, mathematical modeling and computer simulations to show its robust synchronization capabilities which enable efficient communication between the brain regions.

neuroscience↗

bioRxiv2020

Long-term stability of avalanche scaling and integrative network organization in prefrontal and premotor cortex

Ongoing neuronal activity in the cortex establishes functional networks of synchronization that reflect normal and pathological brain function. The reconstruction of these networks typically suffers from the use of indirect measures of neuronal activity at low spatiotemporal resolution and a lack of longitudinal tracking. Accordingly, the precise nature of the underlying synchronization dynamics and its translation into robust graph theoretical markers are not well characterized. Here, we studied the stability of cortical dynamics and reconstructed functional networks over many weeks in prefrontal and premotor cortex of awake nonhuman primates. We monitored neuronal population activity directly in the ongoing local field potential (LFP) at high spatial and temporal resolution using chronically implanted high-density microelectrode arrays. Ongoing activity was composed of neuronal avalanches exhibiting stable, inverted parabolic profiles with the collapse exponent of 2 in line with a critical branching process. Avalanche-based functional networks, reconstructed using a Normalized Count estimator, revealed robust integrative properties characterized by high neighborhood overlap between strongly connected nodes and robustness to weak-link pruning. "Entropy of mixing" analysis demonstrated progressive link reorganization over weeks. The long-term stability of avalanche scaling and integrative network organization should support the development of robust biomarkers to characterize normal and abnormal brain function.

neuroscience↗

bioRxiv2020

A New Framework for MR Diffusion Tensor Distribution

The ability to characterize heterogeneous and anisotropic water diffusion processes within macro-scopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values unlike existing models. We use a Monte Carlo scheme to synthesize realistic numerical diffusion tensor distribution (DTD) phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.

neuroscience↗