DRIADA: an open-source Python toolkit for linking single-neuron selectivity and population activity to behavior
Abstract
Understanding how individual neurons contribute to population activity during behavior is central to understanding brain functions across biological scales. We developed DRIADA, an open-source Python toolkit that connects analyses of individual neurons, population activity, and neuronal networks to the same behavioral parameters. We evaluated it on synthetic populations with known ground truth, calcium-imaging recordings from dorsal hippocampal CA1 in 13 mice across 39 sessions, and a simulated continuous attractor neural network (CANN) with known toroidal organization. Across most hippocampal sessions, embeddings of spatially selective neurons predicted held-out position better than equally sized random subsets and all recorded neurons. The same workflow related population dimensions back to individual neurons, including cells without detected selectivity for the measured behavioral variables. In the simulated network, complementary analyses captured features consistent with its known organization. Thus, DRIADA provides a common framework for relating neuronal selectivity, population structure, and network activity to behavioral dynamics.
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BibTeX
@misc{pospelov2026driada,
title = {{DRIADA: an open-source Python toolkit for linking single-neuron selectivity and population activity to behavior}},
author = {Nikita Pospelov and Viktor Plusnin and Olga Rogozhnikova and Anna Ivanova and Vladimir Sotskov and Margarita Orobets and Ksenia Toropova and Olga Ivashkina and Vladik Avetisov and Konstantin Anokhin},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.00851},
eprint = {2607.00851},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.00851},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - DRIADA: an open-source Python toolkit for linking single-neuron selectivity and population activity to behavior
AU - Nikita Pospelov
AU - Viktor Plusnin
AU - Olga Rogozhnikova
AU - Anna Ivanova
AU - Vladimir Sotskov
AU - Margarita Orobets
AU - Ksenia Toropova
AU - Olga Ivashkina
AU - Vladik Avetisov
AU - Konstantin Anokhin
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2607.00851
UR - https://arxiv.org/abs/2607.00851
AB - Understanding how individual neurons contribute to population activity during behavior is central to understanding brain functions across biological scales. We developed DRIADA, an open-source Python toolkit that connects analyses of individual neurons, population activity, and neuronal networks to the same behavioral parameters. We evaluated it on synthetic populations with known ground truth, calcium-imaging recordings from dorsal hippocampal CA1 in 13 mice across 39 sessions, and a simulated continuous attractor neural network (CANN) with known toroidal organization. Across most hippocampal sessions, embeddings of spatially selective neurons predicted held-out position better than equally sized random subsets and all recorded neurons. The same workflow related population dimensions back to individual neurons, including cells without detected selectivity for the measured behavioral variables. In the simulated network, complementary analyses captured features consistent with its known organization. Thus, DRIADA provides a common framework for relating neuronal selectivity, population structure, and network activity to behavioral dynamics.
ER -