DateFFeb/28(Tue)15:30-17:00
VenueFIIS, University of Tokyo, D Block, Dw601
Invited SpeakerFDr. Michael HerrmanniGeorg-August-University Gottengen  Institute
for Nonlinear Dynamics and Bernstein Center for Computational
Neuroscience)

Title: 

Criticality of Avalanche Dynamics in Recurrent Networks

Abstract:

Neural networks with long-range connectivity are known to display
critical behavior including power-law activity fluctuations. It has been
shown earlier that precisely specified connections strengths are
sufficient to produce this behavior. The predictions of the model have
been observed experimentally both in neural cultures and in slices. In a
more realistic model which includes synaptic dynamics on short time
scales, the system regulates itself to the critical point. A further
step consists of imposing a slow adaptive dynamics to the network which
achieves criticality by a learning process. The critical exponent can be
rigorously derived and confirmed by numerical simulations.