Memory is the way we keep telling ourselves our stories...

Alice Munro

About

About

 

We use tools from machine learning and statistics to figure out how learning and memory happen at the level of neural circuits. 

Studying learning, in brains and machines. 

Interaction between  theory and experiment

Theoretical neuroscience:

How do memories form and what happens when we remember them?

How do we learn about the structure of our sensory world?

How do we know that we don't know?

Data analysis:

Characterizing the statistics of neural activity patterns:

How do neurons work together to achieve circuit function?

How do their interactions change due to learning?

News
  • Febr '21 | Daniel's paper on online hyperparameter optimization is now on arxiv!
  • Jan '21  | 4 posters and 1 contributed talk accepted to Cosyne!
  • Dec '20 | Owen has been awarded the 2021-22 Dean dissertation fellowship!
  • Sept '20 | Edoardo and Colin's papers accepted to NeurIPS 2020.
  • Sept '20 | Strong Savin lab showing at Bernstein conference (3 posters, 2 workshop talks).
  • Febr '20 | Owen's JMLR paper is now online. 
  • Febr '20 | Cristina receives Google faculty award (Kyunghyun Cho co-PI). 
  • Jan '20    | Colin and David's work accepted at Cosyne
  • Oct '19   | Owen and Colin to present their work in NeurIPS 2019 workshops. 
  • Sept '19 | Caroline's paper accepted for the NeurIPS 2019 main meeting. 
  • May '19 | Caroline receives Google fellowship; Camille awarded an NSF GRFP.   
  • Jan '19   | Four posters accepted to Cosyne
  • Dec '18  | Eun Hye Park's paper on head direction coding published in Neuron.
  • July '18  | Paper on unsupervised learning in neural circuits published in Scientific Reports.
  • Sept '17 | New MaxEnt review published in Curr Opin Neurobiol.