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Delgado L, Navarrete M. Shining the Light on Astrocytic Ensembles. Cells 2023; 12:1253. [PMID: 37174653 PMCID: PMC10177371 DOI: 10.3390/cells12091253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
While neurons have traditionally been considered the primary players in information processing, the role of astrocytes in this mechanism has largely been overlooked due to experimental constraints. In this review, we propose that astrocytic ensembles are active working groups that contribute significantly to animal conduct and suggest that studying the maps of these ensembles in conjunction with neurons is crucial for a more comprehensive understanding of behavior. We also discuss available methods for studying astrocytes and argue that these ensembles, complementarily with neurons, code and integrate complex behaviors, potentially specializing in concrete functions.
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Affiliation(s)
| | - Marta Navarrete
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), 28002 Madrid, Spain
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2
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Manninen T, Aćimović J, Linne ML. Analysis of Network Models with Neuron-Astrocyte Interactions. Neuroinformatics 2023; 21:375-406. [PMID: 36959372 PMCID: PMC10085960 DOI: 10.1007/s12021-023-09622-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2023] [Indexed: 03/25/2023]
Abstract
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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Affiliation(s)
- Tiina Manninen
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland.
| | - Jugoslava Aćimović
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland.
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Kim D, Lee JS. Emulating the Signal Transmission in a Neural System Using Polymer Membranes. ACS APPLIED MATERIALS & INTERFACES 2022; 14:42308-42316. [PMID: 36069456 DOI: 10.1021/acsami.2c12166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Neurons are vital components of the brain. When stimulated by neurotransmitters at the dendrites, neurons deliver signals as changes in the membrane potential by ion movement. The signal transmission of a nervous system exhibits a high energy efficiency. These characteristics of neurons are being exploited to develop efficient neuromorphic computing systems. In this study, we develop chemical synapses for neuromorphic devices and emulate the signaling processes in a nervous system using a polymer membrane, in which the ionic permeability can be controlled. The polymer membrane comprises poly(diallyl-dimethylammonium chloride) and poly(3-sulfopropyl acrylate potassium salt), which have positive and negative charges, respectively. The ionic permeability of the polymer membrane is controlled by the injection of a neurotransmitter solution. This device emulates the signal transmission behavior of biological neurons depending on the concentration of the injected neurotransmitter solution. The proposed artificial neuronal signaling device can facilitate the development of bio-realistic neuromorphic devices.
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Affiliation(s)
- Dongshin Kim
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
| | - Jang-Sik Lee
- Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Korea
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Lathuiliere S, Mesejo P, Alameda-Pineda X, Horaud R. A Comprehensive Analysis of Deep Regression. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020; 42:2065-2081. [PMID: 30990175 DOI: 10.1109/tpami.2019.2910523] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, together with the stochastic nature of the optimization procedures, produce notably different results, making extremely difficult to sift methods that significantly outperform others. This situation motivates the current study, in which we perform a systematic evaluation and statistical analysis of vanilla deep regression, i.e., convolutional neural networks with a linear regression top layer. This is the first comprehensive analysis of deep regression techniques. We perform experiments on four vision problems, and report confidence intervals for the median performance as well as the statistical significance of the results, if any. Surprisingly, the variability due to different data pre-processing procedures generally eclipses the variability due to modifications in the network architecture. Our results reinforce the hypothesis according to which, in general, a general-purpose network (e.g., VGG-16 or ResNet-50) adequately tuned can yield results close to the state-of-the-art without having to resort to more complex and ad-hoc regression models.
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Oschmann F, Berry H, Obermayer K, Lenk K. From in silico astrocyte cell models to neuron-astrocyte network models: A review. Brain Res Bull 2017; 136:76-84. [PMID: 28189516 DOI: 10.1016/j.brainresbull.2017.01.027] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 01/30/2017] [Accepted: 01/31/2017] [Indexed: 01/25/2023]
Abstract
The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons.
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Affiliation(s)
- Franziska Oschmann
- Technical University Berlin, Neural Information Processing Group, Sekr. MAR 5-6, Marchstrasse 23, 10587 Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Hugues Berry
- INRIA, 69603 Villeurbanne, France; LIRIS UMR5205, University of Lyon, 69622 Villeurbanne, France
| | - Klaus Obermayer
- Technical University Berlin, Neural Information Processing Group, Sekr. MAR 5-6, Marchstrasse 23, 10587 Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Kerstin Lenk
- Tampere University of Technology, BioMediTech, PL100, 33014 Tampere, Finland.
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Pastur-Romay LA, Cedrón F, Pazos A, Porto-Pazos AB. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications. Int J Mol Sci 2016; 17:E1313. [PMID: 27529225 PMCID: PMC5000710 DOI: 10.3390/ijms17081313] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 07/14/2016] [Accepted: 07/25/2016] [Indexed: 12/20/2022] Open
Abstract
Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure-Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron-Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.
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Affiliation(s)
- Lucas Antón Pastur-Romay
- Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain.
| | - Francisco Cedrón
- Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain.
| | - Alejandro Pazos
- Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain.
- Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña 15006, Spain.
| | - Ana Belén Porto-Pazos
- Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain.
- Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña 15006, Spain.
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Rosselló JL, Alomar ML, Morro A, Oliver A, Canals V. High-Density Liquid-State Machine Circuitry for Time-Series Forecasting. Int J Neural Syst 2016; 26:1550036. [DOI: 10.1142/s0129065715500367] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.
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Affiliation(s)
- Josep L. Rosselló
- Electronics Engineering Group, Physics Department, Universitat de les Illes Balears, Mateu Orfila Building, Cra. Valldemossa km. 7.5, Palma de Mallorca, Balears 07122, Spain
| | - Miquel L. Alomar
- Electronics Engineering Group, Physics Department, Universitat de les Illes Balears, Mateu Orfila Building, Cra. Valldemossa km. 7.5, Palma de Mallorca, Balears 07122, Spain
| | - Antoni Morro
- Electronics Engineering Group, Physics Department, Universitat de les Illes Balears, Mateu Orfila Building, Cra. Valldemossa km. 7.5, Palma de Mallorca, Balears 07122, Spain
| | - Antoni Oliver
- Electronics Engineering Group, Physics Department, Universitat de les Illes Balears, Mateu Orfila Building, Cra. Valldemossa km. 7.5, Palma de Mallorca, Balears 07122, Spain
| | - Vincent Canals
- Electronics Engineering Group, Physics Department, Universitat de les Illes Balears, Mateu Orfila Building, Cra. Valldemossa km. 7.5, Palma de Mallorca, Balears 07122, Spain
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Montani F, Oliynyk A, Fadiga L. Superlinear Summation of Information in Premotor Neuron Pairs. Int J Neural Syst 2015; 27:1650009. [PMID: 26906455 DOI: 10.1142/s012906571650009x] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5.
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Affiliation(s)
- Fernando Montani
- 1 Iflysib, Conicet & Universidad Nacional de La Plata, 59-789 La Plata, Argentina
| | - Andriy Oliynyk
- 2 Section of Human Physiology, Department of Biomedical Sciences and Advanced Therapies, Faculty of Medicine, University of Ferrara, Via Fossato di Mortara 17/19, 44121 Ferrara, Italy
| | - Luciano Fadiga
- 3 IIT@UNIFE Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Ferrara, Italy.,4 Section of Human Physiology, University of Ferrara, Italy
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Ranjbar M, Amiri M. On the role of astrocyte analog circuit in neural frequency adaptation. Neural Comput Appl 2015. [DOI: 10.1007/s00521-015-2112-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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