Seminar

Understanding Neuronal Clustering through Evolution of Adaptation Distribution

Abstract: Abstract: In this presentation, I will explore the influence of various parameters on the oscillation frequencies of neuronal networks, with a focus on a network model consisting of excitatory and inhibitory theta neurons. Central to the discussion will be the impact of adaptation currents in excitatory neurons on oscillation frequency, particularly through inducing clustered firing patterns. The talk will also introduce a discrete map to analyze and visualize the clustering behaviors and adaptation distribution within the network.

On a Network Security Game Model

Vivek Shandilya is an assistant professor in the department of computer science in Bowie State University and directs SOPSS Lab there. He holds a PhD in Computer Science from University of Memphis. His work involves investigating and establishing the structures in the interaction of intelligent agents with conflicting & mutually unknown motivations in stochastic systems. This problem manifests in optimization & security situations of computational, biological, and socio-economic systems.

On the Second Differentiability of Convex Functions

Abstract: In 1939 Alexandrov proved one of the most beautiful results in convex analysis concerning the second order differentiability of a convex function. The result is similar to Rademacher's theorem and states that a convex function is second differentiable almost everywhere. Since the original publication, numerous proofs have been given for this famed theorem and in this talk we will look at the most recent proof by Azagra, Cappello, and Hajlasz.