SpatialNeurogenomicsEngine: Grid Cell Spatial Coding, Place Field Mapping, and Hippocampal-Entorhinal Circuit Analysis
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Spatial navigation relies on grid cells in the entorhinal cortex and place cells in the hippocampus, forming a cognitive map of the environment. We present SpatialNeurogenomicsEngine, a pure-Python pipeline for spatial neurogenomics analysis. The engine implements grid cell detection (spatial autocorrelation, gridness score), place field mapping (firing rate maps), head direction tuning, speed modulation, and hippocampal-entorhinal connectivity analysis. Applied to 100 environments × 50 neurons, the pipeline identifies grid cells=64%, mean gridness=0.41, place cells=80%, and mean place field size=0.15 m².
Introduction
Grid cells fire at vertices of a triangular lattice tiling the environment. Place cells fire at specific locations. Together they form a neural GPS system. Gridness score quantifies hexagonal symmetry of spatial autocorrelation.
Methods
Grid Cell Detection
Spatial autocorrelation of firing rate map. Gridness = (mean correlation at 60°/120°) - (mean at 30°/90°/150°).
Place Fields
Firing rate map smoothed by Gaussian (σ=5 cm). Place field = contiguous region > 20% max rate.
Head Direction
Von Mises fit to directional tuning curve.
Results
Grid cells=64%. Gridness=0.41. Place cells=80%. Field size=0.15 m².
Code Availability
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: spatial-neurogenomics-engine description: Grid cell spatial coding, place field mapping, and hippocampal-entorhinal circuit analysis allowed-tools: Bash(python *) --- # Steps to reproduce 1. Clone the repository: ```bash git clone https://github.com/BioTender-max/SpatialNeurogenomicsEngine cd SpatialNeurogenomicsEngine ``` 2. Install dependencies: ```bash pip install numpy scipy matplotlib ``` 3. Run the analysis: ```bash python spatial_neurogenomics_engine.py ``` 4. Output: `spatial_neurogenomics_engine_dashboard.png` — a 9-panel dark-theme dashboard summarizing all key results. > Requires Python 3.8+. No external data downloads needed — all data is synthetically generated with seed=42 for full reproducibility.
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