vrfcd 0.1.1

van Rossum distance based functional community detection for spike trains

vrfcd

vrfcd stands for van Rossum Functional Community Detection.

This is a Python package for constructing functional connectivity networks from neural spike-train data using the van Rossum distance, and then detecting functional assemblies in the resulting weighted network. The package is designed for computational neuroscience workflows where the input data are spike rasters. The goal is to identify groups of neurons with similar temporal spiking structure.

Workflow

Given a spike train as input the workflow is a four step process:

- Compute the van Rossum distance matrix
- Transform it into functional adjacency matrix
- Prepare the weighted `networkX` graph
- Perform community detection on the graph

Features

vrfcd provides tools to:

• compute a normalised van Rossum distance matrix from spike-train raster datasets,
• convert the distance matrix into a functional adjacency matrix using different similarity transformations,
• construct a weighted functional graph from the adjacency matrix,
• detect communitues using either the Louvain or Leiden community detection algorithm,
• visualise the distance matrix, functional matrix, and network representation, and
• run the full analysis using a single pipeline interface.

Installation

pip install vrfcd

For development:

git clone https://github.com/indranilg49/vrfcd.git
cd vrfcd
pip install -e ".[dev]"

For optional Leiden community detection:

pip install -e ".[dev,leiden]"

Simple usage

import numpy as np
from vrfcd import VRFCDPipeline

pipeline = VRFCDPipeline( t_R=0.01, kernel="minmax", community_method="louvain", n_jobs=4, )

result = pipeline.fit(spike_matrix, t_axis)

D = result.D
A = result.A
G = result.G

partition = result.partition

Here spike_matrix should have the shape of n_neurons x n_time_bins, and t_axis should be the corresponding time axis. Note that if the time axis comes from experimental data with absolute time stamps, it is a good idea to rescale it so that it starts at zero:

t_axis = t_axis - t_axis[0]

Main functions

• compute_van_rossum_distance

from vrfcd import compute_van_rossum_distance

This computes the pairwise van Rossum distance matrix between spike trains.

D = compute_van_rossum_distance(
    spike_matrix,
    t_axis,
    t_R=0.01,
    traces=False,
    n_jobs=4,
)

The parameter t_R controls the time scale of the exponential filter used in the van Rossum distance. Smaller values of t_R make the distance more sensitive to precise spike timing, while larger values smooth spike trains over a longer temporal window.

If traces=True, the function also returns the convolved spike-train waveforms:

D, waveforms = compute_van_rossum_distance( spike_matrix, t_axis, t_R=0.01, traces=True, )

• distance_to_functional_matrix

from vrfcd import distance_to_functional_matrix

This converts the van Rossum distance matrix into a functional adjacency matrix.

A = distance_to_functional_matrix( D, kernel="minmax", )

This package currently supports three similarity transformations.

kernel = "clipping"

When using this, the distances are capped at 1 and converted to similarities using: A = 1-D.

kernel = "exponential"

When using this, the distances are rescaled using an exponential kernel: A = exp(-D/beta). The parameter beta controls how quickly similarity decays with distance.

A = distance_to_functional_matrix( D, kernel="exponential", beta=0.1, )

kernel = "minmax"

When using this, the distances are rescaled using a min-max transformation:

A = distance_to_functional_matrix(
    D,
    kernel="minmax",
    q_low=0.0,
    q_high=1.0,
)

This maps small distances to high functional similarity and large distances to low functional similarity. The diagonal of the resulting matrix is set to 0 by default.

• build_functional_graph

from vrfcd import build_functional_graph

This builds a weighted networkX graph from the functional adjacency matrix.

G = build_functional_graph( A, threshold=0.0, )

In the graph representation, each neuron is a node, and edges are weighted by the functional similarity values in A. The threshold parameter can be used to remove weak functional connections.

• detect_communities

from vrfcd import detect_communities

This function detects communities in the weighted functional graph.

partition = detect_communities( G, method="louvain", seed=42, )

The output is a disctionary mapping each node to a community label. The package currently supports two methods for community detection:

method = "louvain"
method = "leiden"

The Louvain method works with the core dependencies. In order to use Leiden, you will need to install the optional dependencies igraph and leidenalg additionally.

Pipeline interface

Here we outline the easiest way to use the package through VRFCDPipeline.

from vrfcd import VRFCDPipeline 

pipeline = VRFCDPipeline( t_R=0.01, kernel="minmax", q_low=0.0, q_high=1.0, community_method="louvain", n_jobs=4, traces=True, )

result = pipeline.fit(spike_matrix, t_axis)

The pipeline returns a VRFCDResult object containing:

• result.D: van Rossum distance matrix
• result.A: functional adjacency matrix
• result.G: community labels
• result.partition: graph with community attributes
• result.G_partitioned: van Rossum distance matrix
• result.counts: number of nodes in each community
• result.waveforms: convolved spike traces, if traces=True

This allows the full analysis to be run in one step while still giving access to every intermediate object.

Plotting

vrfcd also includes helper functions for viusalisation.

from vrfcd.plotting import plot_matrix, plot_functional_network

To plot the van Rossum distance matrix:

plot_matrix( result.D, colorbar_title=r"$\tilde{D}(t_R)$", savepath="distance_matrix.pdf", )

To plot the adjacency matrix:

plot_matrix( result.A, colorbar_title=r"$A(t_R)$", savepath="functional_matrix.pdf", )

To plot the functional network:

plot_functional_network( result.G_partitioned, partition=result.partition, savepath="functional_network.pdf", )

Overview

This package is intended for exploratory and research-focused analysis of spike-train data. It is particularly useful when one wants to compare neurons based on spike timing, construct a functional network from these similarities, and identify groups of neurons with similar temporal spiking patterns. This package can be used with synthetic spike raster, simulated spiking network models, and experimental recordings such as Neuropixels spike-train datasets.