Learning neural networks

Opening Summer 2021

Exploiting synergies between
Machine Learning and neuroscience to demystify brain functions

Cognitive and computational neuroscience has flourished in recent years with the arrival of innovations enabling us to record and track thousands of neurons for long periods from multiple brain regions. However, the conceptual insights into brain function that we can extract from such data are only as deep as the analysis we can do and the models we can understand. In parallel, artificial intelligence (AI) and the study of in-silico neural networks have undergone a great revolution. Much like their biological counterparts, our understanding of why and how modern Machine Learning (ML) algorithms work lags behind technological achievements. The parallel developments in neuroscience and ML afford exciting research directions to understand how cognition and behavior emerge from circuit dynamics and connectivity.

We aim to exploit synergies between model ML and neuroscience along several principal directions:

  1. Study underlying principles of learning and inference by neural networks with emphasis on biological-plausible implementations

  2. Use artificial neural networks as surrogate brain circuits.

  3. Develop algorithms and theories targeted at specific computational neuroscience needs. We are interested in analyzing large-scale neural data from complex behavioral experiments and designing optical stimulations experiments.

  4. Highlight the differences between modern ML frameworks and their neuronal counterparts to understand the deficiencies and benefits of either.


Our lab is a part of the Edmond and Lily Safra Center for Brain Sciences at the Hebrew University, Israel.

We arenow hiring graduate students and postdoc researchers with a background in physics, math, CS, engineering, or computational biology.