The next Neural Mechanisms Online webinar “Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies” will be delivered by James V. Haxby on June 3rd. See below for details about the free talk and how to join.
Hyperalignment:
Modeling shared information encoded in idiosyncratic cortical topographies
James V. Haxby (Dartmouth College)
h 14-16 Greenwhich Mean Time / 16-18 CEST (check your local time here)
| Join the meeting via Webex (the link will be activated 10 mins before the talk) |
Abstract. Information that is shared across brains is encoded in idiosyncratic fine-scale functional topographies. Hyperalignment captures shared information by projecting pattern vectors for neural responses and connectivities into a common, high-dimensional information space, rather than by aligning topographies in a canonical anatomical space. Individual transformation matrices project information from individual anatomical spaces into the common model information space, preserving the geometry of pairwise dissimilarities between pattern vectors, and model cortical topography as mixtures of overlapping, individual-specific topographic basis functions, rather than as contiguous functional areas. The fundamental property of brain function that is preserved across brains is information content, rather than the functional properties of local features that support that content. In this Perspective, we present the conceptual framework that motivates hyperalignment, its computational underpinnings for joint modeling of a common information space and idiosyncratic cortical topographies, and discuss implications for understanding the structure of cortical functional architecture.
You can find the paper here.
Practical information:
- How to join the session can be found at https://www.neuralmechanisms.org/how-to-connect.html
- Troubleshooting page: https://www.neuralmechanisms.org/troubleshooting.html
Videos of prior talks can be found on the Neural Mechanisms YouTube channel.