Complex/Dynamical Systems Seminar - Zach Kilpatrick
| Event Description: Zach Kilpatrick, Department of Applied Mathematics, °µÍø½ûÇø Dynamics of multilayered neural networks Neuronal activity underlying working memory can be modeled by networks with local excitation and broad inhibition, which support persistent bumps of activity. We consider a specific task where a subject must remember a single analog variable. In this case, fluctuations cause the bump to wander,degrading the fidelity of the memory. Accounting for the multilayer structure of the networks underlying working memory can help to make the dynamics of these bump more robust, in several different ways. We discuss low-dimensional approximations of stationary bump solutions in stochastic neural fields with multiple layers. These reductions describe the position of bumps in each layer of the network. Importantly, these approximations preserve information about the synaptic connectivity of the network, including the spatial heterogeneity, delays, and strength of connectivity between layers. Ìý |
| Location Information: ÌýÌý() 1111 Engineering DR Boulder, CO Room:Ìý226: Applied Math Conference Room |
| Contact Information: Name: Ian Cunningham Phone: 303-492-4668 Email: amassist@colorado.edu |