Multi-scale Physiological Systems

Wednesday, June 16 at 11:30am (PDT)
Wednesday, June 16 at 07:30pm (BST)
Thursday, June 17 03:30am (KST)

SMB2021 SMB2021 Follow Wednesday (Thursday) during the "MS14" time block.
Note: this minisymposia has multiple sessions. The second session is MS15-NEUR (click here).

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Saeed Farjami (University of Surrey, United Kingdom), Anmar Khadra (McGill University, Canada)


Many physiological systems in neurophysiology, immunology and cell biology possess different time scale at the single cell and population levels. The interaction of such time scales allow these systems to exhibit interesting and complex dynamics that are essential for their function. The use of slow-fast analysis to study these muti-scale systems is essential for deciphering their underlying dynamics. In this minisymposium, speakers will present their work on how such approaches allowed us to determine the implications of the presence of different time-scales on functions in many physiological systems in health and disease.

Saeed Farjami

(University of Surrey, United Kingdom)
"Non-sequential Spike Adding in Cerebellar Stellate Cells"
Cerebellar Stellate Cells are spontaneously spiking. Recently, our colleagues have recorded bursting activities in these cells by applying pharmacological agents known for blocking certain ion currents. Such activities are usually modelled in the form of systems with different time scales. When the slow variables are treated as parameters, the fast subsystem can provide good insights into the dynamics of the full model. Using slow-fast analysis, we explain the underlying mechanisms responsible for generating types of bursting emerging in the model. Also, a bifurcation analysis of the full model reveals the effect of different doses of the pharmacological agents on the system dynamics. Moreover, our investigations show that the number of spikes in an active phase of bursting changes when parameters of the system fluctuate. However, in contrast to former studies, adding new spikes does not happen sequentially. In this talk, we will discuss such phenomena and try to shed light on their underlying dynamics.

Michael Forrester

(University of Nottingham, United Kingdom)
"Using a multiscale next-generation neural-mass model to fit neuroimaging data"
Owing to their formulation as exact mean-field representations of neural oscillators, next-generation neural mass models are natural candidates to explore neurological phenomena related to local, and non-local, synchronisation in the brain, such as beta-rebound/beta-burst effects and large-scale functional connectivity. Here, we demonstrate applications of one such model over varying spatial scales and highlight its usefulness in exploring the importance of features of the underlying neuron model, such as gap-junction coupling and synaptic reversal potentials, in emergent large-scale population dynamics.

Victoria Booth

(University of Michigan, USA)
"Dynamics and bifurcations of sleep-wake behavior"
While transitions between sleep and wake states happen quickly, the timing of these transitions are modulated by the slower processes of the 24 h circadian rhythm and the homeostatic sleep drive, the irresistible urge for sleep after being awake. We are developing and analyzing mathematical models of neuronal sleep-wake regulatory networks to understand how the interaction of fast and slow processes dictate the timing and durations of sleep and wake episodes. In this talk, I will discuss recent analyses of solutions of these models based on construction of circle maps that have allowed identification of the bifurcations underlying the transitions of sleep-wake behavior over human development and across species.

Sue Ann Campbell

(University of Waterloo, Canada)
"Time delays may enhance or impede synchronization in brain networks"
We study the effect of time-delays in a neural field model for a brain network. The model considered is a network of Wilson-Cowan nodes with inhibitory weights dynamically modified to represent homeostatic regulation. Without time delay, the system has been shown to exhibit rich dynamics including oscillations, mixed-mode oscillations, and chaos. Synchronization of the nodes depends on the connectivity structure of the network. Using the Master Stability formalism, we show that time delays in the connections between the nodes 1) may stabilize brain dynamics by temporarily preventing the onset to oscillatory and pathologically synchronized dynamics, and 2) may enhance or diminish synchronization depending on the underlying eigenvalue spectrum of the connectivity matrix.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.