Kang Li wins poster award – University of Copenhagen

Dynamical Systems > News > Kang Li wins poster award

10 June 2016

Kang Li wins poster award


PhD student Kang Li won last week an award for best poster at a conference on mathematical neuroscience.

Kang Li won a Kindle and 200 Euros to buy Springer-books. 72 posters were participating in the competition. Kang Li’s poster had the title: "Responses of leaky integrate-and-fire neurons to a plurality of stimuli".

The conference "2nd International Conference on Mathematical Neurosciencedsin" was held in Juan-les-Pins on the French Riviera. The main speakers were Wulfram Gerstner (Switzerland), John Rinzel (USA) and Antoine Triller (France). From MATH participated, in addition to Kang Li, Professor Susanne Ditlevsen, PhD-student Jacob Østergaard and Research Assistant Mads Bonde Raad.

Kang Li has been a PhD student at the Department of Mathematical Sciences since November 2013. He is affiliated with the section Statistics and Probability Theory; Susanne Ditlevsen is his principal advisor.

Li is also affiliated with the Dynamical Systems Interdisciplinary Network (DSIN), working with mathematical modeling and statistical methods in social, health and science. DSIN’s aim is to develop mathematical models and statistical methods to describe and understand complex dynamic systems in these areas.

Resume from Kang Li’s poster:

“A fundamental question concerning the way the visual world is represented in our brain is how a cortical cell responds when its classical receptive field contains a plurality of stimuli. Two opposing models have been proposed. In the response-averaging model, the neuron responds with a weighted average of all individual stimuli. By contrast, in the probability-mixing model, the cell responds to a plurality of stimuli as if only one of the stimuli were present. Here we apply the probability-mixing and the response-averaging model to leaky integrate-and-fire neurons, to describe neuronal behavior based on observed spike trains. We first estimate the parameters of either model using numerical methods, and then test which model is most likely to have generated the observed data. Results show that the parameters can be successfully estimated and the two models are distinguishable using model selection.”