Mathematical Biology Seminar
Department of Mathematical Sciences

New Jersey Institute of Technology

Fall 2012

 

All seminars are 4:00 - 5:00 p.m., in Cullimore Hall Room 611 (Math Conference Room) unless noted otherwise. Refreshments are usually served at 3:30 p.m., and talks start at 4:00 p.m. If you have any questions about a particular seminar, please contact the person hosting the speaker. The Math Department also hosts a number of other seminars and colloquia which can be accessed here: DMS Seminar Listing

 

Date

Speaker and Title

Host

 Tuesday

 September 25
 
4:00PM

Erol Gelenbe, Imperial College 

The random neural network: theory and applications

Amit Bose

Tuesday
October 2
4:00PM


No Seminar



Tuesday
October 9
4:00PM


Sashi Marella,  NJIT

Neuromodulation of cortical interneurons in epileptic animal models

Amit Bose

Tuesday
October 16
4:00PM


Society for Neuroscience Meeting - No Seminar


Tuesday
October 23
4:00PM

Allen Tannenbaum, University of Alabama-Birmingham

Mathematical Methods in Medical Image Computing

Horacio Rotstein

Tuesday
October 30
4:00PM

Katherine Newhall - New York University
Synchronous Firing Events in Stochastic Neuronal Network Models

Amit Bose

Tuesday
November 6
1:00PM

Boydon 421

Dirk Bucher -  University of Florida

Joint Seminar with Department of Biological Sciences

Farzan Nadim

Tuesday
November 13
4:00PM

Miranda I. Teboh-Ewungkem - Lafayette College

POSTPONED UNTIL SPRING SEMESTER

Amit Bose

Tuesday
November 20
4:00PM

 Markus A. Dahlem  - Humboldt University Berlin

Migraine: A Dynamical Disease

Robert Miura

Tuesday
November 27
4:00PM


UBM Student Presentations

Victor Matveev

Tuesday
December 4
4:00PM


Tomasz Smolinski, 
Delaware State University  

Analyzing conductance correlations involved in recovery of bursting after
neuromodulator deprivation in lobster stomatogastric neuron models

Farzan Nadim

Tuesday
December 11
4:00PM


 UBM Student Presentations

Amit Bose

 

Abstracts

Erol Gelenbe, Imperial College 

The random neural network: theory and applications

This talk will introduce the random neural network model (RNN) and some of its extensions, for instance to synchronised interactions. We will also discuss applications both in biology and engineering. These include cortico-thalamic oscillations, protein alignment, video compression, image texture generation and combinatorial optimization.


Sashi Marella,  NJIT
Neuromodulation of cortical interneurons in epileptic animal models

In this talk, I will present some recent experimental findings on putative modulation of cortical interneurons activity in animal (vertebrate) models of seizure and epilepsy. Changes in receptor density for neuropeptide Y (NPY) in these animal models implicated in experimentally observed modulation of excitatory and inhibitory inputs onto cortical interneurons will be discussed.  Theoretical questions regarding the functional significance of these  observations to the putative cortical circuit consisting of pyramidal neurons and fast-spiking interneurons will be motivated.

Allen Tannenbaum
, University of Alabama-Birmingham
Mathematical Methods in Medical Image Computing


In this talk, we will describe some theory and practice of controlled active vision. The  applications range from visual tracking (e.g., laser tracking in turbulence, flying in  formation of UAVs, etc.), nanoparticle flow control, and sedation control in the intensive care unit. Our emphasis will be on the medical side,especially image guided therapy
 and surgery. This includes projectssuch as radiation planning in cancer therapy, traumatic brain injury, and left atrial fibrillation. We concentrate on two key areas: segmentation and registration.  For segmentation, we will describe several models of active contours for which both local and global information may be included. We will indicate how statistical estimation and prediction  ideas (e.g., particle filtering) may be naturally combined with this methodology. For registration,  we propose using ideas from optimal mass transport (Monge-Kantorovich) and uniformization theory from Riemann surfaces. The techniques we invoke come from curvature driven flows,
differential geometry, control theory, and the calculus of variations; they will be demonstrated on a wide variety of data sets from various medical imaging modalities.

Markus A. Dahlem, Humboldt University Berlin

Migraine: A Dynamical Disease

The key to the genesis of migraine is an emergent phenomenon called cortical spreading depression (SD).  SD is a transient state that during its course massively perturbs ionic gradients across cells membranes. A generic reaction-diffusion mechanism with global inhibition is presented by which localized, long-lasting but transient SD wave patterns are formed. The distinct transient waves are caused by a ghost of a saddle-node bifurcation, i.e., a bottle-neck passage in phase space associated with a characteristic form (shape, size) that depends critically on the curvature of the cortex. Similar patterns have been observed with fMRI. We correlate the patterns with biological pathways for the pain formation in migraine and investigate means by which neuromodulation techniques
may affect these pathways.

Katherine Newhall, New York University

Synchronous Firing Events in Stochastic Neuronal Network Models

Synchrony manifests itself in a variety of forms in noisy biological systems.  Within computational neuronal models, even with the desynchronizing effect of noisy input, the excitatory coupling between neurons can cause the network to synchronize, or oscillate, over a large range of model parameters.  I will discuss synchrony in the context of pulse-coupled Integrate-and-Fire models where cascading firing events cause multiple neurons to fire at the exact same instance in time.  I will describe the synchronizing mechanism and present methods for computing the probability distribution for the number of neurons firing together, as well as the probability the system maintains a synchronous firing state.  These results can potentially be combined with population based simulation methods to efficiently simulate interacting excitatory and inhibitory neuron populations in biologically relevant regimes.

Tomasz Smolinski Delaware State University

Analyzing conductance correlations involved in recovery of bursting after neuromodulator deprivation in lobster stomatogastric neuron models

In the crustacean stomatogastric ganglion (STG) functional neuronal activity can be produced with widely varying cellular parameter values. One possible mechanism responsible for this phenomenon is coregulation of ionic currents, such as the coregulation of the delayed rectifier K+ current, IKd, and the transient Ca2+ current, ICaT, which affects the peak and duration of the slow-wave oscillation in bursting STG neurons. Interestingly, ionic current coregulation depends on neuromodulation, not activity. This is intriguing because the STG can recover function after neuromodulator deprivation (i.e., deafferentation). After the stomatogastric nerve, via which the STG receives neuromodulatory inputs, is cut or blocked, STG neurons initially lose their function. However, within 24h to 96h, without external intervention, they again exhibit activity similar to that in intact STGs. The interplay between deafferentation, function recovery, and coregulation of ionic currents is under active research, which has so far produced interesting results showing that while some relationships are lost due to neuromodulator deprivation, some of them are altered (presumably to support recovery). Here, we use a computational approach to study these phenomena in an important STG neuron, the anterior burster (AB). Previously, we explored a 12-dimensional parameter space of an AB model by simulating 21,600,000 parameter combinations. Every parameter set was simulated and classified as functional if it produced realistic bursting activity and properly responded to deafferentation (i.e., became quiescent). Out of the ~400,000 models that failed the second criterion we selected those that showed bursting (~14,000). We consider those models “recovered,” as they function despite neuromodulation deprivation. By analyzing the parameters of the “recovered” neurons, we investigate the impact of deafferentation on coregulation of ionic currents. We show, for example, that the relationship between IKd and ICaT is preserved regardless of the presence of neuromodulation, although the slope of the relationship seems to be altered.