arXiv subjects

Viktor Plusnin

Publications and source records attributed to Viktor Plusnin.

3 recordsLinked to original sources

arXiv2026

DRIADA: A Python Toolkit for Cross-Scale Analysis of Single-Neuron Selectivity and Population Dynamics

Brain activity spans single-neuron, population, and network levels, and core questions in neural coding require moving between them. Yet current tools target a single paradigm and incompatible data formats, leaving cross-level questions hard to address. We present DRIADA, an open-source Python framework that unifies neural signals and time-aligned behavior in a shared data model, so selectivity testing, dimensionality reduction, and network analysis operate within a unified workflow. We evaluate it on synthetic data with known ground truth, hippocampal calcium imaging from 13~mice in an open field, and a simulated toroidal attractor network. In the hippocampal data, selectivity-based filtering restored a two-dimensional spatial embedding from a collapsed all-neuron embedding, while reverse analysis showed that ${\sim}57\%$ of neurons informative about leading manifold dimensions were not selective to any of the 11 measured behavioral features. On the toroidal benchmark, four independent modules recovered the expected topology. DRIADA makes cross-scale analysis routine across calcium imaging, spike trains, and simulated networks.

q-bio.NC↗

arXiv2026

INTENSE: Detecting and disentangling neuronal selectivity in calcium imaging data

Neurons encode information about the environment through their activity. As animals explore the environment, neurons rapidly acquire selectivity for distinct features of the external world; characterizing how these selectivity patterns emerge, reorganize, and overlap is key to linking neural activity to behavior and cognition. Calcium imaging in freely behaving animals can record large neuronal populations, but quantifying neuron-behavior selectivity directly from continuous fluorescence is challenging because both signals are temporally autocorrelated and calcium kinetics introduce time lags. Here we present INTENSE (INformation-Theoretic Evaluation of Neuronal SElectivity), an open-source framework that uses mutual information to detect neuron-behavior associations from raw calcium fluorescence data. INTENSE controls false discoveries using circular-shift permutation testing that preserves temporal structure and optimizes temporal delays to account for indicator kinetics and prospective/retrospective encoding. To separate genuine mixed selectivity from associations driven by behavioral covariance, INTENSE applies conditional mutual information-based disentanglement. We validated INTENSE on synthetic datasets, demonstrating robust detection across diverse signal-to-noise ratios and reliability conditions, whereas methods lacking temporal controls show poor performance. Applied to CA1 miniscope recordings in mice freely exploring an open field, INTENSE reveals robust selectivity to multiple variables (place, head direction, object interaction, locomotion) and refines mixed-selectivity estimates by distinguishing redundant from genuinely multi-variable encoding. Together, INTENSE enables high-throughput, information-theoretic selectivity mapping with principled control of temporal structure and behavioral covariance, bridging large-scale recordings to circuit-level hypotheses.

q-bio.NC↗

arXiv2024

Multiple Neuronal Specializations Elicited By Socially Driven Recognition Of Food Odors

This study investigates the dynamics of non-spatial specializations in hippocampal place cells during exposure to novel environments. Hippocampal place cells, known for their role in spatial mapping, exhibit multi-modal responses to sensory cues. The research focuses on understanding how these cells adapt their specialization in response to novel stimuli, specifically examining non-spatial determinants such as odors and social interactions. Using a social-driven food odor recognition model in mice, the study records CA1 hippocampal neuron activity through miniscope imaging. The experimental design involves demonstrations of novel odors to mice, followed by observation sessions with food options. The analysis employs deep neural network tools for behavior tracking and the custom-developed INTENS software package for identifying neural specializations. Results indicate multiple specializations, particularly those related to odor, with differences observed between training and testing sessions. The findings suggest a temporal aspect to the formation of these specializations in novel conditions, necessitating further investigation for precise tracking.

q-bio.NC↗