driada-cross-scale-neural-analysis

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