Скачать или смотреть Eve Bodnia | Geometry of Machine Learning Workshop lecture

The Geometry of Machine Learning
9/17/2025

Speaker: Eve Bodnia, Logical Intelligence

Abstract: We introduce a method of topological analysis on spiking correlation networks in neurological systems. This method explores the neural manifold as in the manifold hypothesis, which posits that information is often represented by a lower-dimensional manifold embedded in a higher-dimensional space. After collecting neuron activity from human and mouse organoids using a micro-electrode array, we extract connectivity using pairwise spike-timing time correlations, which are optimized for time delays introduced by synaptic delays. We then look at network topology to identify emergent structures and compare the results to two randomized models – constrained randomization and bootstrapping across datasets. In histograms of the persistence of topological features, we see that the features from the original dataset consistently exceed the variability of the null distributions, suggesting that the observed topological features reflect significant correlation patterns in the data rather than random fluctuations. In a study of network resiliency, we found that random removal of 10 % of nodes still yielded a network with a lesser but still significant number of topological features in the homology group H1 (counts 2-dimensional voids in the dataset) above the variability of our constrained randomization model; however, targeted removal of nodes in H1 features resulted in rapid topological collapse, indicating that the H1 cycles in these brain organoid networks are fragile and highly sensitive to perturbations. By applying topological analysis to neural data, we offer a new complementary framework to standard methods for understanding information processing across a variety of complex neural systems.