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VERSION:2.0
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BEGIN:VEVENT
DTSTAMP:20260520T224112Z
UID:https://www.cbs.mpg.de/events/45144/338749
DTSTART:20260409T130000Z
DTEND:20260409T140000Z
CLASS:PUBLIC
CREATED:20260317T162403Z
DESCRIPTION:The attention mechanism\, central to the transformer architectu
 re\, has emerged as a powerful and scalable computational motif underlying
  many recent advances in machine learning. Beyond its success in AI\, atte
 ntion offers a compelling framework for building interpretable\, mechanist
 ically grounded models of brain function. In this talk\, I will present a 
 line of work exploring this potential across encoding and decoding. I will
  begin with Transformer brain encoders\, in which attention is used to mod
 el how retinotopic visual features are dynamically routed to category-sele
 ctive areas in high-level visual cortex\, achieving state of the art encod
 ing performance. I will then describe how this framework was extended to c
 onstruct a digital twin of the visual system\, enabling in silico experime
 nts that reveal categorical visual selectivity across the whole brain. A t
 hird line of work leverages the in-context learning property of transforme
 rs to build encoding models that generalize across subjects and datasets. 
 Finally\, I will present our work on interpretable decoding models that re
 construct images from brain activity using attention-based architectures.\
 nSpeaker: Dr Hossein Adeli
LAST-MODIFIED:20260317T162403Z
LOCATION:MPI for Human Cognitive and Brain Sciences\, Room: virtual
ORGANIZER;CN=CBS CoCoNUT:mailto:%0D
SUMMARY:Guest Lecture: Dr Hossein Adeli | Brain Encoding and Decoding with 
 Transformer Attention
URL;VALUE=URI:https://www.cbs.mpg.de/events/45144/338749
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