Michael Taynnan Barros

Lecturer (Assist. Prof.)

University of Essex

United Kingdom

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About

Michael Taynnan Barros is a Lecturer (Assist. Prof.) at the University of Essex, UK. His research focuses on multiscale biophysics, in-silico pharmacology, and neural networks, with recent articles exploring the dynamics of neuron–astrocyte networks and molecular communication systems. His work also extends to biocomputing models and applications in bio-nano technology.

Articles (14)

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2024

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2024

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2024

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2024

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2023

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2023

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2022

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2022

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2022

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2022

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2021

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2021

Collaborators (11)

Aapo Tervonen

University of Tampere

FINLAND

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Daniel Perez Martins

Lecturer

University of Essex

UNITED KINGDOM

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Masamitsu Kanada

Assistant Professor

Michigan State University

UNITED STATES

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Jari Hyttinen

University of Tampere

FINLAND

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Massimiliano Pierobon

University of Nebraska - Lincoln

UNITED STATES

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Kerstin Lenk

Assistant Professor

Graz University of Technology

AUSTRIA

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Sasitharan Balasubramaniam

Associate Professor

University of Nebraska - Lincoln

UNITED STATES

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Nicola Marchetti

IRELAND

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Jussi Koivumäki

University of Tampere

FINLAND

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Pasquale Stano

University of Salerno

ITALY

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Antoni Llopis-Lorente

Eindhoven University of Technology

NETHERLANDS

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Michael Taynnan Barros

Research Interests

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About

Articles (14)

From Multiscale Biophysics to Digital Twins of Tissues and Organs: Future Opportunities for in-silico Pharmacology

Astrocytes induce desynchronization and reduce predictability in neuron–astrocyte networks cultured on microelectrode arrays

In silico modelling of neuron signal impact of cytokine storm-induced demyelination

Investigating Biologically Plausible Neural Networks for Reservoir Computing Solutions

Biocomputing Model Using Tripartite Synapses Provides Reliable Neuronal Logic Gating With Spike Pattern Diversity

An Adaptable Lateral Resolution Acoustic Beamforming for the Internet of Bio-Nano Things in the Brain

Dual Ionic Transport Using Ca2+ and Na2+ Signaling for Molecular Communication Systems

Toward Interdisciplinary Synergies in Molecular Communications: Perspectives from Synthetic Biology, Nanotechnology, Communications Engineering and Philosophy of Science

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks

Microfluidic-Based Bacterial Molecular Computing on a Chip

Intelligent Dynamic Indoor Aerosol Sensing Using Terahertz Band Wireless Communication Systems

Applying Intelligent Reflector Surfaces for Detecting Violent Expiratory Aerosol Cloud using Terahertz Signals

Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions

Larger Connection Radius Increases Hub Astrocyte Number in a 3-D Neuron–Astrocyte Network Model

Collaborators (11)

Aapo Tervonen

Daniel Perez Martins

Masamitsu Kanada

Jari Hyttinen

Massimiliano Pierobon

Kerstin Lenk

Sasitharan Balasubramaniam

Nicola Marchetti

Jussi Koivumäki

Pasquale Stano

Antoni Llopis-Lorente

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