CT02 - NEUR-1
Suzanne M. Scharer Room (##3146) in The Ohio Union

NEUR Subgroup Contributed Talks

Tuesday, July 18 at 2:30pm

SMB2023 SMB2023 Follow Tuesday during the "CT02" time block.
Room assignment: Suzanne M. Scharer Room (##3146) in The Ohio Union.
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Allison Cruikshank

Duke University
"Dynamical Questions in Volume Transmission"
In volume transmission (or neuromodulation) neurons do not make one-to-one connections to other neurons, but instead simply release neurotransmitter into the extracellular space from numerous varicosities. Many well-known neurotransmitters including serotonin (5HT), dopamine (DA), histamine (HA), Gamma-Aminobutyric Acid (GABA) and acetylcholine (ACh) participate in volume transmission. Typically, the cell bodies are in one volume and the axons project to a distant volume in the brain releasing the neurotransmitter there. We introduce volume transmission and describe mathematically two natural homeostatic mechanisms. In some brain regions several neurotransmitters in the extracellular space affect each others' release. We investigate the dynamics created by this comodulation in two different cases: serotonin and histamine; and the comodulation of 4 neurotransmitters in the striatum and we compare to experimental data. This kind of comodulation poses new dynamical questions as well as the question of how these biochemical networks influence the electrophysiological networks in the brain.
Additional authors: Janet Best, Ohio State University; H. Frederick Nijhout, Duke University; Michael Reed, Duke University

Gurpreet Jagdev

Toronto Metropolitan University
"The interplay between asymmetric noise and uneven coupling of two coupled neuronal oscillators"
Two ubiquitous components, coupling and noise, may drive complex neural networks to exhibit emergence dynamics. While the roles of equal coupling and symmetric noise have been extensively studied, the general mechanisms of unequal coupling strength and asymmetric noise remain unclear. In this work, we investigate the simultaneous interplay of unequal coupling and asymmetric noise in the simplest network motif of two bi-directionally coupled neural oscillators, each with its own intrinsic noise. Our findings show that noise-induced synchrony can be maximized when one oscillator (source) with weak intrinsic noise is strongly connected to the other oscillator (receiver) with strong intrinsic noise. Furthermore, we extend our study to three coupled neural oscillators with a feed-forward-loop schematic. These results shed new light on the complex interplay between coupling and noise in neural networks.
Additional authors: Na Yu, Toronto Metropolitan University

Marina Chugunova

University of Waterloo, Canada
"Modelling duality of the exocytosis initiation in GnRH neurons"
Gonadotropin-releasing hormone (GnRH) neurons work as a trigger of the reproductive axis in mammals. These neurons exhibit two types of exocytosis: a surge and a pulsatile one. Traditionally, changes in the neuron dynamics are connected to and explained by changes in parameters of the action potential, transmitted by a neuron's membrane. However, in case of GnRH neurons, the experimental data demonstrates that the switch in the type of the hormone release is determined rather by the location of the GnRH neuron activation. Action potential initiated in the proximity of soma is necessary for the surge of GnRH. The second type, the pulsatile release of GnRH, is driven by the synaptic activities on the distal part of the neurons. Both types of the exocytosis initiation target the intracellular calcium dynamics. The increase in calcium ions due to the electrical spikes near soma is short-lived. On the other hand, the increase in calcium ions in the distal parts of the GnRH neurons lasts for tens of minutes. We have built the mathematical and computational models of the electrical and chemical dynamics in GnRH neurons. The model, in silico, reveals the connection between the action potential, neuropeptides, and calcium ion dynamics. In addition, our model confirms the functionality of the bundling between multiple GnRH neurons and its effect on exocytosis synchronization.
Additional authors: Sue Ann Campbell; Matthew Harris

Zhuo-Cheng Xiao

New York University
"Efficient models of the cortex via coarse-grained interactions and local response functions"
Modeling the human cortex is challenging due to its structural and dynamic complexity. Spiking neuron models can incorporate many details of cortical circuits but are computationally costly and difficult to scale up, limiting their scope to small patches of cortex and restricting the range of phenomena that can be studied. Alternatively, one can use simpler phenomenological models, which are easier to build and run but are more difficult to compare directly to experimental data. This talk presents an efficient modeling strategy that aims to strike a balance between biological realism and computational efficiency. The proposed modeling strategy combines a coarse-grained representation with local circuit dynamics to compute the steady-state cortical response to external stimuli. A crucial observation is that as a consequence of anatomical structures and the nature of neuronal interactions, potential local responses can be computed independently of dynamics on the coarse-grained level. We first precompute a library of steady-state local responses driven by possible lateral and external input. Then, the fixed point of the coarse-grained model can be computed by an iterative scheme combined with fast library lookup. Our approach is tested on a model of primate primary visual cortex (V1) and successfully captures essential V1 features such as orientation selectivity. Time permitting, I will also discuss a related project in which we devised an efficient way to explore the parameter space of a primate V1 model, identifying the set of viable parameters as a 'thickened' codimension-1 submanifold of parameter space.
Additional authors: Kevin K. Lin, Department of Mathematics, University of Arizona; Lai-Sang Young, Courant Institute of Mathematical Sciences, New York University

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Annual Meeting for the Society for Mathematical Biology, 2023.