| name | driada-cross-scale-neural-analysis |
| description | DRIADA: Open-source Python toolkit for cross-scale analysis of single-neuron selectivity and population dynamics. Unifies neural signals and behavior in shared data model for selectivity testing, dimensionality reduction, and network analysis. Activation: DRIADA toolkit, cross-scale neural analysis, single-neuron selectivity, population dynamics, hippocampal calcium imaging, neural coding toolkit, information-theoretic selectivity. |
| tags | ["neuroscience","computational-toolkit","neural-coding","calcium-imaging","population-dynamics","selectivity-analysis","python-framework","hippocampal-analysis"] |
| version | 1.0.0 |
| author | agent |
| arxiv_id | 2607.00851 |
| paper_title | DRIADA: A Python Toolkit for Cross-Scale Analysis of Single-Neuron Selectivity and Population Dynamics |
| authors | ["Nikita Pospelov","Viktor Plusnin","Olga Rogozhnikova","Anna Ivanova","Vladimir Sotskov"] |
| date | 2026-07-01 |
| subjects | ["q-bio.NC"] |
DRIADA: Cross-Scale Neural Analysis Toolkit
Core Innovation
Problem
Brain activity spans single-neuron, population, and network levels. Core questions in neural coding require moving between these scales. However, existing tools target single paradigms with incompatible data formats, making cross-level questions hard to address.
Solution
DRIADA — an open-source Python framework that:
- Unifies neural signals and time-aligned behavior in a shared data model
- Enables selectivity testing, dimensionality reduction, and network analysis within a unified workflow
- Bridges individual neuron characterization with population structure and functional network analysis
Methodology
Unified Data Representation
- Neural signals (spike trains, calcium traces) mapped to a consistent format
- Time-aligned behavioral events integrated into same data structure
- Cross-session tracking via CellReg-matched neurons
Information-Theoretic Selectivity Testing
- Quantifies how individual neurons encode behavioral features
- Conservative statistical thresholds for reliable within-session detection
- Tested on hippocampal CA1 neurons across 13 mice in open field
Dimensionality Reduction
- Population-level structure analysis
- Reveals nonlinear manifold structure in neural population activity
- Validated on toroidal attractor network simulations with known ground truth
Network Analysis
- Functional connectivity analysis from neural time series
- Graph-based analyses organized around single unified network representation
- Bridges selectivity findings with population-level functional networks
Key Findings
Hippocampal Selectivity Landscape
- Single-feature dominance: Of neurons selective to ≥1 feature, 90.1% selective to exactly one feature (contrasts with strong mixed selectivity in PFC)
- Representational drift: Only 1.1% run-selective and 0.3% place-selective neurons retained labels across all 3 sessions
- Systematic organization: Feature prevalence rank ordering consistent across all 13 mice (Kendall's W = 0.53)
Scale-Bridging Insights
- Population analysis benefits from individual neuron characterization
- Nominally non-selective neurons contribute collectively to spatial manifold
- Cross-scale analysis reveals organizational properties invisible at single scale
