Skip to content
Search events. View events.
 

All Categories

Welcome to the CU-Boulder Events Calendar.

Click for help in using calendar displays. Print the contents of the current screen.

Advanced Search

(New Search)
  From:
  To:



Submit
Event Details
Notify me if this event changes.Add this event to my personal calendar.Email this event to a friend.
Go Back
APPM Complex/Dynamical Systems Seminar - Yogesh Virkar
Start Date: 3/23/2017Start Time: 2:00 PM
End Date: 3/23/2017End Time: 3:00 PM
This event recurs on Thursday every week until 5/4/2017.   Click here to see the series dates.
Event Description:
Yogesh Virkar, Department of Computer Science, University of Colorado Boulder

Effects of multilayer network interactions on neural network dynamics

Networks of excitable units are found in varied disciplines such as social science, neuroscience, genetics, epidemiology, etc. Previous studies have shown that some aspects of network function can be optimized when the network operates in the `critical regime', i.e., at the boundary between order and disorder where the statistics of node excitations correspond to those of a classical branching process. We study the long-standing problem of determining the mechanisms by which the brain regulates its activity to operate in this regime. In particular, we study the dynamics of a two-layered network model consisting of an excitable node network and a complementary network that supplies resources required for node firing. More specifically, we study the dynamics of an excitable neural network consisting of neurons (nodes) connected via synapses (edges). Synaptic strengths are mediated by resources supplied by the complementary glial cell network. Resources from the bloodstream are supplied to the glial network at some fixed rate, resources transport diffusively within the glial cell network and ultimately to the synapses, and each time a presynaptic neuron fires the resources for all outgoing synapses get consumed at some fixed rate. We show that this natural and very compelling mechanism for feedback control can stabilize the critical state. Additionally, the neural network can learn and remember while staying critical. The critical state is characterized by power-law distributed avalanche sizes that are robust to changes in the supply, consumption and diffusion rates. Finally, we show that our findings are fairly robust to heterogeneity in model parameters or network structure.
Location Information:
Main Campus - Engineering Office Tower  (View Map)
1111 Engineering DR
Boulder, CO
Room: 226: Applied Math Conference Room
Contact Information:
Name: Ian Cunningham
Phone: 303-492-4668
Email: amassist@colorado.edu

Calendar Software powered by Dude Solutions   
Select item(s) to Search
Select item(s) to Search
Select item(s) to Search
Select item(s) to Search

Featured Events

Today's Events