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SUMMARY:From Connectivity Matrices to Rate Dynamics - Dr Merav Stern (Univ
ersity of Washington)
DTSTART;VALUE=DATE-TIME:20190401T123000Z
DTEND;VALUE=DATE-TIME:20190401T133000Z
UID:https://talks.ox.ac.uk/talks/id/0c36c0c8-d08c-49de-99aa-17c0ba97c8a7/
DESCRIPTION:Mean-field theory is commonly used to analyze the dynamics of
large neural network models. In this approach\, the interactions of the or
iginal network are replaced by appropriately structured noise driving unco
upled units in a self-consistent manner. This allows properties of the net
work dynamics to be predicted and the behavior of the network to be unders
tood as a whole. Results in random matrix theory have been used to relate
the structure of the connectivity of neural networks to their mean-field d
ynamics. In my talk I will explain the mean-field approach\, discuss its r
elation to random matrix theory\, and analyze how the dynamics of neural n
etwork models are related to their connectivity structure. I will provide
examples of networks that the mean-field theory describes accurately as we
ll as examples\, analyzed with the use of matrix theory\, in which small m
odifications in the connectivity matrix can result in large deviations fro
m mean-field predictions. \nSpeakers:\nDr Merav Stern (University of Washi
ngton)
LOCATION:Le Gros Clark Building (Lecture Theatre)\, off South Parks Road O
X1 3QX
URL:https://talks.ox.ac.uk/talks/id/0c36c0c8-d08c-49de-99aa-17c0ba97c8a7/
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DESCRIPTION:Talk:From Connectivity Matrices to Rate Dynamics - Dr Merav St
ern (University of Washington)
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