Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

TL;DR

This paper introduces a robust adversarial dictionary learning framework for analyzing spontaneous neuronal activity, enabling noise-resistant pattern discovery and low-dimensional representation of complex brain data.

Contribution

It presents a novel adversarial training-based dictionary learning method tailored for noise robustness in neural activity analysis, improving pattern recognition in neuroscience data.

Findings

Enhanced classification accuracy over existing methods

Effective low-dimensional representation of neural data

Rich information extraction from learned dictionaries

Abstract

The field of neuroscience is experiencing rapid growth in the complexity and quantity of the recorded neural activity allowing us unprecedented access to its dynamics in different brain areas. The objective of this work is to discover directly from the experimental data rich and comprehensible models for brain function that will be concurrently robust to noise. Considering this task from the perspective of dimensionality reduction, we develop an innovative, robust to noise dictionary learning framework based on adversarial training methods for the identification of patterns of synchronous firing activity as well as within a time lag. We employ real-world binary datasets describing the spontaneous neuronal activity of laboratory mice over time, and we aim to their efficient low-dimensional representation. The results on the classification accuracy for the discrimination between the clean and the adversarial-noisy activation patterns obtained by an SVM classifier highlight the efficacy of the proposed scheme compared to other methods, and the visualization of the dictionary's distribution demonstrates the multifarious information that we obtain from it.