Spike Structure Analysis

This repository contains the code developed for my Mathematics Final Degree Project.

The project focuses on comparing experimental neural recordings with Spiking Neural Networks (SNN) simulations to evaluate whether the model reproduces both the statistical properties and the population-level structure of neural activity.

The simulated data is generated using the model proposed in Tomé et al. (2024).
The experimental data comes from the CRCNS hc-11 dataset (Buzsáki lab), using a single recording session from the animal Achilles (hippocampal CA1).

Repository structure

• data/ Raw and processed datasets

• analysis/ Reusable functions

• 00_dataset_structure.ipynb Dataset inspection and structure

• 01_hc11_EDA_preprocessing.ipynb Preprocessing and exploratory analysis of experimental data

• 02_snn_EDA_preprocessing.ipynb Preprocessing and exploratory analysis of simulated data

• 03_statistical_comparison.ipynb Statistical validation (firing rate, ISI, CV(ISI), Fano factor, autocorrelation)

• 04_geometric_comparison.ipynb Structural validation using PCA and intrinsic dimensionality (ABID)

Approach

The comparison is carried out using a subsampling-based framework:

• Matched population sizes between data and simulations
• Empirical distributions of metrics
• Monte Carlo hypothesis testing

References

• Tomé, D.F., Zhang, Y., Aida, T. et al. (2024)
  Dynamic and selective engrams emerge with memory consolidation.
  Nature Neuroscience 27, 561–572
  https://doi.org/10.1038/s41593-023-01551-w

• Grosmark, A.D., Long, J., Buzsáki, G. (2016)
  Recordings from hippocampal area CA1 during novel spatial learning.
  CRCNS.org (hc-11 dataset, Buzsáki lab)
  http://dx.doi.org/10.6080/K0862DC5