Research

At Neurone Lab we study computation across biological and artificial systems, aiming to understand how structure, dynamics, and information flow give rise to intelligent behaviour. We also use computational and data-driven methods to analyse biological processes and images, helping biologists interpret complex data and uncover mechanistic insights that are difficult to access through experimental techniques alone. Our work brings together machine learning, computational biology, advanced microscopy, and neuroscience to explore shared principles of learning and representation.

Our research spans integrating ideas from natural systems into artificial intelligence systems and the analysis of complex biological datasets. We work with multi-scale data, including single-cell omics from cancer studies, large collections of label-free cellular images, and high-resolution microscopy modalities such as ptychography. This integrated perspective allows us to link structure, function, and computation while developing algorithms, analysis pipelines, and benchmarks that support both biological discovery and technological innovation. Our interdisciplinary approach connects fundamental research with real-world applications across biology and medicine.

In our NeuroAI research efforts, we develop models, analytical tools, and deep learning methods inspired by biological organization and dynamics, seeking connections between the way nervous systems process information and how artificial systems can learn more efficiently and effectively.

We use a variety of novel genomics and computational methods, developing new approaches when needed to answer the questions. Our current projects include state-of-the art proteomics and microscopy approaches to address the mechanistic principles of how cis-regulatory elements control cell fate choice between neural progenitors.

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