Astrocyte to Neuron Communication Channels With Applications

Mesiti, Fabio; Floor, Palers Anders; Balasingham, Ilangko

doi:10.1109/tmbmc.2015.2501743

Cited by 16 publications

(22 citation statements)

References 35 publications

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“…Half of the neuron-astrocyte network models were so-called generic models. Others, however, were specified to model neuron-astrocyte interactions in the cortex (Allegrini et al, 2009 ; Liu and Li, 2013a ; Chan et al, 2017 ; Tang et al, 2017 ; Yao et al, 2018 ), hippocampus (Amiri et al, 2012a , 2013a ; Mesiti et al, 2015a ; Li et al, 2016c ), spinal cord (Yang and Yeo, 2015 ), or thalamocortical networks (Amiri et al, 2012b , c ).…”

Section: Resultsmentioning

confidence: 99%

“…The modeling strategies for neurons varied depending on the author. Three of the studied publications utilized Hodgkin and Huxley ( 1952 ) model (Liu and Li, 2013b ; Li et al, 2016c ; Yao et al, 2018 ) and one utilized Traub et al ( 1991 ) model's derivative Pinsky and Rinzel ( 1994 ) model (Mesiti et al, 2015a ). Simpler phenomenological models used in the studied publications were the FitzHugh-Nagumo (FitzHugh, 1961 ) model (Postnov et al, 2009 ; Hayati et al, 2016 ), LIF (Gerstner and Kistler, 2002 ) model (Liu and Li, 2013a ; Naeem et al, 2015 ), Izhikevich ( 2007 ) model (Allegrini et al, 2009 ; Haghiri et al, 2016 , 2017 ; Tang et al, 2017 ), Morris and Lecar ( 1981 ) model or its derivatives (Amiri et al, 2012a , 2013a ; Chan et al, 2017 ), and Suffczynski et al ( 2004 ) neuronal population model (Amiri et al, 2012b , c ).…”

Section: Resultsmentioning

confidence: 99%

“…These models make the assumption that chemical species have the same concentrations throughout the entire volume of the astrocyte, which can also be called as average concentrations. However, it has been shown that the concentration of certain chemical species, such as Ca 2+ , can vary drastically in different parts of the cell (see e.g., Thul, 2014 ; Dupont et al, 2016 ), and several models took into account the spatiality by modeling diffusion of at least some of the chemical species in astrocytes using partial differential equations (PDEs, see e.g., Roth et al, 1995 ; Höfer et al, 2002 ; Bennett et al, 2008a ; Gibson et al, 2008 ; Allegrini et al, 2009 ; Kang and Othmer, 2009 ; Edwards and Gibson, 2010 ; Wei and Shuai, 2011 ; Li et al, 2012 ; López-Caamal et al, 2014 ; Mesiti et al, 2015a ; Yao et al, 2018 ). Some models took a simpler approach to spatiality by modeling ODEs combined with fluxes between different astrocytic compartments, such as cytosol and ER (see e.g., Larter and Craig, 2005 ; Di Garbo et al, 2007 ; Postnov et al, 2007 ; Lavrentovich and Hemkin, 2008 ; Di Garbo, 2009 ; Zeng et al, 2009 ; Amiri et al, 2011a ; DiNuzzo et al, 2011 ; Farr and David, 2011 ; Oschmann et al, 2017 ; Kenny et al, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

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Computational Models for Calcium-Mediated Astrocyte Functions

Manninen

Havela

Linne

2018

Front. Comput. Neurosci.

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The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro, but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain. Due to the large number of models, we concentrated on a hundred models that include biophysical descriptions for calcium signaling and dynamics in astrocytes. We categorized the models into four groups: single astrocyte models, astrocyte network models, neuron-astrocyte synapse models, and neuron-astrocyte network models to ease their use in future modeling projects. We characterized the models based on which earlier models were used for building the models and which type of biological entities were described in the astrocyte models. Features of the models were compared and contrasted so that similarities and differences were more readily apparent. We discovered that most of the models were basically generated from a small set of previously published models with small variations. However, neither citations to all the previous models with similar core structure nor explanations of what was built on top of the previous models were provided, which made it possible, in some cases, to have the same models published several times without an explicit intention to make new predictions about the roles of astrocytes in brain functions. Furthermore, only a few of the models are available online which makes it difficult to reproduce the simulation results and further develop the models. Thus, we would like to emphasize that only via reproducible research are we able to build better computational models for astrocytes, which truly advance science. Our study is the first to characterize in detail the biophysical and biochemical mechanisms that have been modeled for astrocytes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Computational Models for Calcium-Mediated Astrocyte Functions

Manninen

Havela

Linne

2018

Front. Comput. Neurosci.

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“…3 for details). In the co-authored papers [42], [43], we proposed communications models of tripartite linear and time-invariant synapses including the astrocytic feedback and modulation of activities of both the pre-and post-synaptic terminals.…”

Section: B Synaptic Channelmentioning

confidence: 99%

“…Several theoretical studies of "physical layer" in synaptic communication have been developed in this decade to understand how single neurons integrate, process and communicate electrochemical and molecular signals [22], [24], [97]- [99], and interact with the surrounding glia [42], [43]. Important models have evolved within the EU project MINERVA: Communication Theoretical Foundations of Nervous System Towards Bio-Inspired Nanonetworks and ICT-Inspired Neuro-Treatment (FP7-IDEAS-ERC #616922) [25], [34], [100], [101].…”

Section: B Future Cognitive Brain-machine Interfacesmentioning

confidence: 99%

Synaptic Communication Engineering for Future Cognitive Brain–Machine Interfaces

Veletić

Balasingham

2019

Proc. IEEE

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Disease-affected nervous systems exhibit anatomical or physiological impairments that degrade processing, transfer, storage, and retrieval of neural information leading to physical or intellectual disabilities. Brain implants may potentially promote clinical means for detecting and treating neurological symptoms by establishing direct communication between the nervous and artificial systems. Current technology can modify neural function at the supracellular level as in Parkinson's disease, epilepsy, and depression. However, recent advances in nanotechnology, nanomaterials, and molecular communications have the potential to enable brain implants to preserve the neural function at the subcellular level which could increase effectiveness, decrease energy consumption, and make the leadless devices chargeable from outside the body or by utilizing the body's own energy sources. In this study, we focus on understanding the principles of elemental processes in synapses to enable diagnosis and treatment of brain diseases with pathological conditions using biomimetic synaptically interactive brain-machine interfaces. First, we provide an overview of the synaptic communication system, followed by an outline of brain diseases that promote dysfunction in the synaptic communication system. We then discuss technologies for brain implants and propose future directions for the design and fabrication of cognitive brain-machine interfaces. The overarching goal of this paper is to summarize the status of engineering research at the interface between technology and the nervous system and direct the ongoing research towards the point where synaptically interactive brain-machine interfaces can be embedded in the nervous system.

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Analysis of Network Models with Neuron-Astrocyte Interactions

2023

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Neural networks, composed of many neurons and governed by complex interactions between them, are a widely accepted formalism for modeling and exploring global dynamics and emergent properties in brain systems. In the past decades, experimental evidence of computationally relevant neuron-astrocyte interactions, as well as the astrocytic modulation of global neural dynamics, have accumulated. These findings motivated advances in computational glioscience and inspired several models integrating mechanisms of neuron-astrocyte interactions into the standard neural network formalism. These models were developed to study, for example, synchronization, information transfer, synaptic plasticity, and hyperexcitability, as well as classification tasks and hardware implementations. We here focus on network models of at least two neurons interacting bidirectionally with at least two astrocytes that include explicitly modeled astrocytic calcium dynamics. In this study, we analyze the evolution of these models and the biophysical, biochemical, cellular, and network mechanisms used to construct them. Based on our analysis, we propose how to systematically describe and categorize interaction schemes between cells in neuron-astrocyte networks. We additionally study the models in view of the existing experimental data and present future perspectives. Our analysis is an important first step towards understanding astrocytic contribution to brain functions. However, more advances are needed to collect comprehensive data about astrocyte morphology and physiology in vivo and to better integrate them in data-driven computational models. Broadening the discussion about theoretical approaches and expanding the computational tools is necessary to better understand astrocytes’ roles in brain functions.

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Astrocyte to Neuron Communication Channels With Applications

Cited by 16 publications

References 35 publications

Computational Models for Calcium-Mediated Astrocyte Functions

Computational Models for Calcium-Mediated Astrocyte Functions

Synaptic Communication Engineering for Future Cognitive Brain–Machine Interfaces

Analysis of Network Models with Neuron-Astrocyte Interactions

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