Data Science seminars

Assessing how brain activity generalizes across individuals is a central challenge in experimental neuroscience. Traditional task- or stimulus-driven approaches align data through trial averaging and anatomical registration, but these methods fail for spontaneous activity, where no shared temporal reference exists. In this talk, I will introduce a statistical framework, called latent-aligned Restricted Boltzmann Machines, to build a common representational space from whole-brain recordings of spontaneous activity in multiple zebrafish larvae. This shared latent space, composed of spatially localized co-activation motifs or cell assemblies, allows bidirectional mapping of brain states: activity patterns from one fish can be encoded and decoded into another. The translated activity patterns retain their original spatial structure and show high plausibility within the recipient brain. We further use this shared space to segment spontaneous activity into discrete brain states and we quantify their Markovian transition statistics. Remarkably, these state-to-state dynamics are stereotyped across individuals, suggesting that spontaneous activity reflects intrinsic computational priors of neural processing. Together, these results demonstrate how probabilistic generative modeling can bridge individual variability and reveal conserved organizational principles of vertebrate brains. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2025-11-05. Recording duration: 00:36:37.

Susan Athey examines how machine learning and digital interventions can help explain and improve workers’ transitions between jobs. One project uses Swedish administrative data to identify groups whose earnings and employment are less resilient after layoffs, revealing substantial differences among workers within the same firms and labour markets. A second line of work uses transformer models and large language models to represent careers and analyse gender wage gaps, identifying settings in which large unexplained differences persist. Two further projects develop and evaluate digital interventions intended to help disadvantaged workers enter expanding occupations in information technology and data science. The lecture connects new methods for measuring labour-market disadvantage with evidence about practical interventions.

Rocco Servedio surveys algorithms that determine whether very large objects possess a property, or are far from having it, by examining only a small part. The lecture covers progress on classic problems and newer testing frameworks motivated by machine-learning theory and high-dimensional data analysis.

Nearest-neighbor indexes usually rely on a single distance function, but accurate comparisons can be expensive. This talk proposes a bi-metric framework: a cheap proxy metric builds the index, while the query procedure uses a limited number of evaluations of both the proxy and an expensive ground-truth metric. The theory applies to DiskANN and Cover Tree. When the proxy approximates the ground-truth metric within a bounded factor, the resulting structure can achieve arbitrarily good approximation guarantees under the accurate metric. Experiments on text retrieval using models with very different computational costs show improved accuracy-efficiency tradeoffs on almost all MTEB datasets compared with alternatives such as reranking. Joint work with Haike Xu and Sandeep Silwal.

Learning induces changes in neural activity over slow timescales. These changes can be summarized by restructuring neural population data into a three-dimensional array or tensor, of size neurons by time points by trials. Classic dimensionality reduction methods often assume that neural representations are constrained to a fixed low-dimensional latent subspace. Consequently, this view does not capture how the latent subspace could evolve over learning, nor how high-dimensional neural activity could emerge over learning. Furthermore, the link between these empirically-observed changes in neural activity as a result of learning and circuit-level changes in recurrent dynamics is unclear. In this talk I will discuss our recent efforts towards developing dimensionality reduction and data-driven modeling methods based on tensors in order to identify how neural representations change over learning. First we introduce a new tensor decomposition, sliceTCA, which is able to disentangle latent variables of multiple covariability classes that are often mixed in neural population data. We demonstrate in three datasets that sliceTCA is able to capture more behaviorally-relevant information in neural data than previous methods. Second, to probe for how circuit-level changes in neural dynamics implement the observed changes in neural activity, we develop a data-driven RNN-based framework in which the recurrent connectivity is constrained to be low tensor rank. We demonstrate that such low tensor rank RNNs (ltrRNNs) are able to capture changes in neural geometry and dynamics in motor cortical data from a motor adaptation task. Together, both sliceTCA and ltrRNN demonstrate the utility of interpretable, tensor-based methods for discovery of learning-induced changes in neural representations directly from data. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2024-01-24. Recording duration: 00:45:54.