Place Cell Methods Project

Overview repository for the 'Place Cell Methods' project.

This repository maps out the openly available code and analyses reported in the Place Cell Methods project & paper.

Overview

The place cell methods project: Evaluating Methods for Place Cell Detection in Rats and Humans

The paper & project includes a series of investigations across multiple empirical datasets as well as with simulations, all of which have openly available code repositories demonstrating the analyses, which are described and linked here.

Literature Analyses

The place cells methods project includes some literature analyses to examine the use of different methods to characterize place cells. This part of the project is included in the current repository, in the literature folder.

This folder supports:

• Table 1: Rat Place Cell Studies
• Table 2: Human Place Cell Studies

Rat Data Analyses

The place cell methods comparisons were applied to rat single-neuron data, which is available in the Analyze_RAThc3 repository.

This repository covers:

• Figure 1c, Example Rat Place Cells
• Figure 2, Place Cell Identification Methods in Rat
• Figure 4a, Statistical distribution of place cell measures in rats
• Figure 7abc, PCA Analysis on Rat Place Cell's Estimated Features
• Figure S1a: SI threshold and permutation methods identify different subsets of neurons in rats
• Figure S3a,b,e,f,i,j,m: Robustness of spatial tuning metrics across different spatial binning resolutions in rats
• Figure S4a,b,c: Relationship between spatial tuning metrics and firing properties in rats
• Figure S5a: Pairwise correlations among neural features and their relationship to spatial tuning metrics in rats
• Figure S7a: PCA with feature visualization of neural features in rats, Direct Link: https://github.com/HSNPipeline/Analyze_RAThc3

Human Data Analyses

The place cell methods comparisons were applied to human single-neuron data, which is available in the AnalyzeTrain repository.

This repository covers:

• Figure 1c, Example Human Place Cells
• Figure 3, Place Cell Identification Methods in Human
• Figure 4b, Statistical distribution of place cell measures in humans
• Figure 7d, PCA Analysis on Human Place Cell's Estimated Features
• Figure S1b: SI threshold and permutation methods identify different subsets of neurons in humans
• Figure S2: Effect of smoothing on human place cell measures.
• Figure S3c,d,g,h,k,l,n: Robustness of spatial tuning metrics across different spatial binning resolutions in humans
• Figure S4d,e,f: Relationship between spatial tuning metrics and firing properties in humans
• Figure S5b: Pairwise correlations among neural features and their relationship to spatial tuning metrics in humans
• Figure S6a: Principal component analysis of spatial coding metrics in human neurons.
• Figure S7b: PCA with feature visualization of neural features in humans

Direct Link: https://github.com/HSNPipeline/AnalyzeTrain

Simulations

The place cell methods were also tested on simulated data, which is available in the SimPlaceCells repository.

This repository covers:

• Figure 5, Place Field Simulation Framework
• Figure 6, Impact of place field features on spatial information and ANOVA statistics
• Figure 7d, PCA Analysis on Simulated Place Cell's Estimated Features
• Figure S4g,h,i: Relationship between spatial tuning metrics and firing properties in simulations
• Figure S5c: Pairwise correlations among neural features and their relationship to spatial tuning metrics in simulations
• Figure S6b: Principal component analysis of spatial coding metrics in neurons.
• Figure S7c: PCA with feature visualization of neural features in simulations. Direct Link: https://github.com/HSNPipeline/SimPlaceCells