Allen Database Notebooks

A collection of Jupyter notebooks for analyzing neuroscience data from the Allen Institute.

Setup Instructions

Prerequisites

• Python 3.11 (recommended but any should do)
• Anaconda or Miniconda for environment management

Installation

1. Create a Python environment:

   conda create -n allen-notebooks python=3.11
   conda activate allen-notebooks

2. Install requirements:

   pip install -r requirements.txt

3. Install additional packages from GitHub (with fixes for Allen Institute compatibility):

   git clone https://github.com/GregGlickert/neuroanalysis
   cd neuroanalysis
   python setup.py develop
   cd ..

   git clone https://github.com/GregGlickert/aisynphys
   cd aisynphys
   python setup.py develop
   cd ..

Usage

This repository contains several Jupyter notebooks for analyzing neuroscience data:

Synapses

Synapses/chemical_synapses.ipynb

Analyzes chemical synaptic properties between cell types in mouse visual cortex. The notebook:

• Loads excitatory synapse data from the Allen Institute database
• Defines cell classes (ET, IT, PV, SST neurons)
• Extracts and analyzes synaptic parameters
• Fits stochastic release models

Synapses/electrical_synapses.ipynb

Examines electrical synapses (gap junctions) in inhibitory neuron subclasses. The notebook:

• Queries gap junction data for PV, SST, and VIP cells
• Measures junctional conductance
• Visualizes conductance distributions across cell types

Cells

Cells/in_vivo_cell_properties.ipynb

Examines electrophysiological properties of individual neurons from the Allen Cell Types Database. The notebook:

• Filters cells by species, brain region (Layer 5 visual cortex), and transgenic lines
• Extracts key electrophysiological features (input resistance, membrane time constant, etc.)
• Generates visualizations of cell type-specific characteristics such as FI curve and current clamps

Connections

Connections/connections.ipynb

Analyzes synaptic connectivity between cell classes in mouse visual cortex (V1). The notebook:

• Loads cell pair data from the Allen Institute's synaptic physiology database
• Defines cell classes based on cortical layer and transgenic markers
• Computes raw and distance-adjusted connection probabilities with fitted Gaussian models
• Visualizes connectivity profiles and distance-dependent connectivity
• Estimates synaptic convergence using uniform cell distribution assumptions