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DESCRIPTION:Structure\, Function\, and Learning in Distributed Neuronal Net
 works\n\nSueYeon Chung - NYU / Flatiron Institute\n	Tuesday April 19\, 12-1
 pm\n	Zoom Link: https://mcgill.zoom.us/j/85428056343\n\nAbstract: A central
  goal in neuroscience is to understand how orchestrated computations in th
 e brain arise from the properties of single neurons and networks of such n
 eurons. Answering this question requires theoretical advances that shine l
 ight into the ‘black box’ of neural networks. In this talk\, we will demon
 strate theoretical approaches that help describe how cognitive and behavio
 ral task implementations emerge from structure in neural populations and f
 rom biologically plausible learning rules.\n\nFirst\, we will introduce an
  analytic theory that connects geometric structures that arise from neural
  responses (i.e.\, neural manifolds) to the neural population’s efficiency
  in implementing a task. In particular\, this theory describes the shatter
 ing capacity for linearly classifying object categories based on the under
 lying neural manifolds’ structural properties.\n\nNext\, we will describe 
 how such methods can\, in fact\, open the ‘black box’ of distributed neura
 l networks\, by showing how we can understand a) the role of network motif
 s in task implementation in neural networks and b) the role of neural nois
 e in adversarial robustness in vision and audition.\n\nFinally\, we will d
 iscuss our recent efforts to develop biologically plausible learning rules
  for deep neural networks\, inspired by recent experimental findings in sy
 naptic plasticity. By extending our mathematical toolkit for analyzing rep
 resentations and learning rules underlying complex neuronal networks\, we 
 hope to contribute towards the long-term challenge of understanding the ne
 uronal basis of tasks and behaviors.\n
DTSTART:20220419T160000Z
DTEND:20220419T170000Z
SUMMARY:QLS Seminar - SueYeon Chung
URL:/qls/channels/event/qls-seminar-sueyeon-chung-3391
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