Methematical modeling and Data science seminar

On the efficiency of neuronal information processing

Speaker: Lubomir Kostal (Czech Academy of Sciences)

Please and Date: Room 1316A, 11:00 on 28th February, 2019

Abstract: The research in computational neuroscience has a tradition of more than 100 years, marked, e.g., by the now-classical Lapicque, McCulloch-Pitts or Hodgkin-Huxley neuronal models. During the last three decades the field has experienced a dramatic increase, attracting a number of scientists from different disciplines. New topics have emerged alongside the traditional neuronal modeling approaches and the long-standing problem of neuronal coding is recently receiving substantial attention. The approach to the problem relies on the applications of information theory, signal detection and estimation theory and theory of stochastic processes to different aspects of neuronal information processing, including coding and decoding in individual neurons and populations, or analysis of beneficial role of the noise in the system. Understanding the principles of information processing in neurons may help to introduce, e.g., new algorithms or new generation of hardware which could enhance artificial sensors.

Modelling structure and predicting dynamics of discussion threads in online boards using Hawkes processes

Speaker: Alexey Medvedev (Univ. Namur)

Please and Date: Room 1316B, 14:00 on 26th January, 2018

Abstract: Online social platforms provide a fruitful source of information about social interaction. Depending on the platform, various tree-like cascading patterns emerge as a consequence of such interaction. For example, on Twitter or on Facebook people interact via resharing messages, which turns into cascade trees of reshares, in email networks people forward messages to their peers resulting in trees of email forwards, in online boards like Digg or Reddit people interact via discussing particular posts, which leaves a trace of discussion trees. The two main questions arise: what is the shape of these cascades and how to predict the dynamics of their evolution? The question of evolution of discussion threads is now gradually being understood. By now researchers studied only the structural evolution of discussion trees and the dynamical properties are left out of consideration. We note there was proposed a sort of a mean-field model for dynamics and structure, however the average nature of the model has limited utility in practice. We consider cascades given by discussion trees of posts in online board Reddit. The dataset of Reddit discussion threads consists of all posts and comments submitted to Reddit from Jan, 2008 till Jan, 2015. The dataset in total contains more than 150 million posts and around 1.4 billion comments. We propose a model of discussion trees generation based on the self-exciting Hawkes processes, which represents both the tree structure and temporal information. We use the dataset of Reddit discussion threads to show that structurally trees resemble Galton-Watson trees with a special root offspring distribution, and distinct the cases when the dynamics of comments attraction can be well predicted using Hawkes processes.