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Tiselko, V.

Publications and source records attributed to Tiselko, V..

2 recordsLinked to original sources

Non-redundant role of the a3 isoform of Na,K-ATPase in neuronal excitability and spiking dynamics

The Na+,K+-ATPase (NKA) plays a fundamental role in neuronal excitability by maintaining ionic gradients and contributing to electrogenic resting currents. Among its isoforms, the neuron-specific 3 subunit exhibits a uniquely low affinity for intracellular Na+, weak voltage dependence, and slightly reduced ATP sensitivity compared to the ubiquitous 1. Although mutations in ATP1A3 are known to cause severe neurological disorders, the specific kinetic features of 3 that underlie its functional specialization remain incompletely understood. Here we employed biophysically detailed models of stretch receptor neurons, grounded in patch-clamp recordings of the 3-isoform current and spiking responses, to dissect the contribution of isoform-specific pump kinetics to firing behavior. Substitution of 1 for 3 abolished the ability to sustain long spike trains and reduced high-frequency entrainment, whereas 3-preserved prolonged discharges and faithful responses to vibratory stimuli. Remarkably, even halved pump density 350% preserved superior excitability compared to mixed expression (350%/150%), indicating that kinetic profile rather than pump quantity determines firing capacity. Hybrid models revealed that Na+ affinity is the decisive factor: retaining the low Na+ affinity of 3 preserved excitability, while introducing 1-like voltage or ATP dependence produced only minor effect on simulated neuron discharge. These findings establish a mechanistic explanation for the selective expression of 3 in muscle spindle afferents and other neurons with high-frequency demands, and help to explain why 1 cannot compensate in ATP1A3-linked diseases. More broadly, they highlight the principle that isoform specialization of the NKA is not redundant but tuned to the discharge requirements of distinct neuronal populations.

neuroscience↗

Neural Receptive Fields, Stimulus Space Embedding and Effective Geometry of Scale-Free Networks

Understanding how receptive fields emerge and organize within brain networks and how neural dynamics couple with stimuli space is fundamental to neuroscience. Models often rely on fine-tuning connectivity to match empirical data, which may limit biological plausibility. Here we propose a physiologically grounded alternative where receptive fields and population-level attractor dynamics arise naturally from the effective hyperbolic geometry of scale-free networks. By associating stimulus space with the boundary of a hyperbolic embedding, we simulate neural dynamics using rate-based and spiking models, revealing localized activity patterns that reflect stimulus space structure without synaptic fine-tuning. The resulting receptive fields follow experimentally observed statistics and properties, and their sizes depends on neurons connectivity degree. The model generalizes across stimuli dimensionalities and various modalities, such as orientation and place selectivity. Experimental analyses of hippocampal place fields recorded on a linear track support these findings. This framework offers a novel organizing principle linking network structure, stimulus space encoding, and neural dynamics, providing insights into receptive field formation across diverse brain areas.

neuroscience↗