University of Cambridge > Talks.cam > Computational Neuroscience > Quantifying individuality in neural circuit representations

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Quantifying individuality in neural circuit representations

Download to your calendar using vCal

If you have a question about this talk, please contact Daniel Kornai .

Signatures of neural computation are thought to be reflected in the coordinated activity of large neural populations. Neuroscience is now flush with measurements of these activity patterns in humans, animal subjects, and large-scale artificial network models. In this talk, I will address an extensively studied, yet unresolved, question: How should we quantify the extent to which two or more neural circuits have “similar” activation patterns? Without an answer to this question, the field has struggled to investigate basic questions about biological variability and individuality, such as: How do neural representations vary across a healthy population? How do differences in neural population activity correlate with behavioral idiosyncrasies and disorders? How similar are computational mechanisms in biological brains and artificial neural networks? In this talk, I will summarize several mathematical methods that quantify similarity in neural representations and demonstrate how they provide early insights into these questions when applied to biological data and artificial networks.

This talk is part of the Computational Neuroscience series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Event will be by invitation only. see below CMS, Potter Room (B1.19) Martin Centre Research Seminar Series - 47st Annual Series of Lunchtime Lectures

Other talks

Alicia Anderson discussing some exciting news about HARPS3 TBC Not-so-noble gases, key tracers of Earth’s habitability: what their behaviour in planetary interiors can tell us? Machine Learning for Spectral Geometry and Vice Versa Title to be confirmed Shape modes of CP^1 vortices
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity