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Tuesday, July 21 • 7:25pm - 7:55pm
W3 S6: Characterising Sensory and Abstract Representations in Neural Ensembles

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Many recent advances in artificial intelligence (AI) are rooted in visual neuroscience. However, ideas from more complicated tasks like decision-making are less used. At the same time, decision making tasks that are hard for AI are easy for humans. Thus, understanding human brain dynamics during these tasks could improve AI performance.
Here we modelled some of these dynamics. We investigated how they flexibly represented and distinguished between sensory processing and categorization in two sensory domains: motion direction and color. We used two different approaches for understanding neural representations. We compared brain responses to 1) the geometry of a sensory or category domain (domain selectivity) and 2) predictions from deep neural networks (computation selectivity). Both approaches gave us similar results. Using the first approach, we found that neural representations changed depending on context. We then trained deep recurrent neural networks to perform the same tasks as the animals. Using the second approach, we found that computations in different brain areas also changed flexibly depending on context. Both approaches yielded the same conclusions: decision making in the color domain appeared to rely more on sensory processing, while in the motion domain more on abstract representations. Finally, using biophysical modeling, and data from a spatial delayed response task, we characterized cortical connectivity in neural ensembles and explained a well-known behavioral effect in psychophysics, known as the oblique effect.
Overall, this talk will introduce an approach for studying the computations and neural representations taking place in neural ensembles by exploiting a combination of machine learning, biophysics and brain imaging.


Dimitris Pinotsis

Associate Professor & Research Affiliate, University of London - City & MIT

Tuesday July 21, 2020 7:25pm - 7:55pm CEST
Crowdcast (W03)