Reconstructing dynamical models from optogenetic data

Oprisan, Sorinel A.; Lynn, Patrick; Tompa, Tamás; Lavín, Antonieta

doi:10.1186/1471-2202-16-s1-p143

Cited by 2 publications

(1 citation statement)

References 4 publications

(3 reference statements)

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“…Optogenetics has many applications, for instance, it allows controlling functions of neural cells in epilepsy, depression, and tumors of the central nervous system (Camporeze et al, 2018). Optogenetic data can be very useful for reconstructing dynamical models of brain dynamics (Oprisan et al, 2015) and for imaging and manipulating brain networks (Forli et al, 2021). Together with electrophysiological data, it provides the selfassembled multifunctional neural probes as a powerful tool for investigating causal relationships between neural circuit activity and function (Zou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Speed Gradient Control Algorithm for Optogenetic Modeling

Borisenok¹

2021

European Journal of Science and Technology

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We discuss the feedback algorithm for the optogenetic control over the membrane conductance in the frame of Grossman-Nikolic-Toumazou-Degenaar (GNTD) ordinary differential system modeling the response of channelrhodopsin-2 (ChR2) expressing neurons to the light stimulation with the various types of ChR2 mutants. The GNTD population dynamics contains four functional states (two open and two closed) with the transitions among them due to photo-excitations with the stimulating light or decays back from the open to the closed states. The control signal in the model is defined via the photon flux per one ChR2 in the dimensionless form. The control goal is the total conductance of a neural section due to ChR2. We formulate the control algorithm in the form of Fradkov's speed gradient method driving the dynamical system in the phase space such that the target function for the discrepancy between the actual total conductance and its desired level is minimized. We derive the explicit equation for the photon flux field stabilizing the conductance characteristics and perform the numerical simulation for the controlled GNTD differential system to prove the achievability of the control goal. Our approach can be useful for modeling different experimental problems of optogenetics, particularly, for driving the collective dynamics of neural cells in epilepsy, depression, and tumors of the central nervous system.

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Section: Introductionmentioning

confidence: 99%

Speed Gradient Control Algorithm for Optogenetic Modeling

Borisenok¹

2021

European Journal of Science and Technology

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The diverse club

2017

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A complex system can be represented and analyzed as a network, where nodes represent the units of the network and edges represent connections between those units. For example, a brain network represents neurons as nodes and axons between neurons as edges. In many networks, some nodes have a disproportionately high number of edges as well as many edges between each other and are referred to as the “rich club”. In many different networks, the nodes of this club are assumed to support global network integration. Here we show that another set of nodes, which have edges diversely distributed across the network, form a “diverse club”. The diverse club exhibits, to a greater extent than the rich club, properties consistent with an integrative network function—these nodes are more highly interconnected and their edges are more critical for efficient global integration. Finally, these two clubs potentially evolved via distinct selection pressures.

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Reconstructing dynamical models from optogenetic data

Cited by 2 publications

References 4 publications

Speed Gradient Control Algorithm for Optogenetic Modeling

Speed Gradient Control Algorithm for Optogenetic Modeling

The diverse club

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