  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.
