Events

October 1, 2017 at 4:30 pm

Math-Biology Seminar | Emergent Stochastic Oscillations and Signal Detection in Tree Networks of Excitable Elements, Oct. 31

Dr.Alexander Neiman

Dr. Alexander Neiman

The Mathematical Biology and Dynamical Systems Seminar presents Dr. Alexander Neiman discussing “Emergent stochastic oscillations and signal detection in tree networks of excitable elements” on Tuesday, Oct. 31, from 3:05 to 4 p.m. in Morton 218.

Neiman is Professor of Physics & Astronomy at Ohio University. Collaborators on his research are Ali Khaledi Nasab, a physics graduate student at Ohio University, along with Justus Kromer and  Lutz Schimansky-Geier from Humboldt University at Berlin.

Abstract:  We study  dynamics of stochastic excitable elements coupled on  tree networks. In our setup the peripheral nodes receive independent random inputs which may induce large spiking events propagating through the branches of the tree. For strong enough coupling firing of peripheral nodes  become synchronized,  leading to global coherent oscillations in the network. This scenario may be  relevant to generation of action potential in certain sensory neurons,  which possess myelinated distal dendritic tree-like arbors and may exhibit noisy periodic sequences of action potentials.

A biophysical model of distal branches of a sensory neuron in which  nodes of Ranvier at peripheral and branching points are coupled  by myelinated cable segments is used along with a generic model of networked stochastic active rotators. We focus on the spiking statistics of the central node, which fires in response to independent noisy  inputs at peripheral nodes. We show that, in the strong coupling regime, relevant to myelinated dendritic trees, the spike train statistics can be predicted from an equivalent excitable element with rescaled parameters according to the network topology. Furthermore, we show that by varying the network topology the statistics of network firing can be tuned to have a certain firing rate and variability, or to allow for an optimal discrimination of inputs applied at the peripheral nodes.

 

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