Nuno R. B. Martins, PhD

Scientific Papers


Dr. Martins research paper “Human Brain Cloud Interface” is on the TOP 10 MOST VIEWED PAPERS EVER on Frontiers of neuroscience. Being on the TOP 10 is a serious recognition from the scientific community of the importance of this paper, in particular because Frontiers of Neuroscience has more than 10,105 (ten thousand one hundred and five) published research papers. Frontiers of Neuroscience is currently the world’s most cited journal in the field of neuroscience, ahead of “Nature” and the journal “Neuron”. 

Human Brain/Cloud Interface

Abstract: The Internet comprises a decentralized global system that serves humanity’s collective effort to generate, process, and store data, most of which is handled by the rapidly expanding cloud. A stable, secure, real-time system may allow for interfacing the cloud with the human brain. One promising strategy for enabling such a system, denoted here as a “human brain/cloud interface” (“B/CI”), would be based on technologies referred to here as “neuralnanorobotics.” Future neuralnanorobotics technologies are anticipated to facilitate accurate diagnoses and eventual cures for the ~400 conditions that affect the human brain. Neuralnanorobotics may also enable a B/CI with controlled connectivity between neural activity and external data storage and processing, via the direct monitoring of the brain’s ~86 x 10^9 neurons and ~2 x 10^14 synapses. Subsequent to navigating the human vasculature, three species of neuralnanorobots (endoneurobots, gliabots, and synaptobots) could traverse the blood–brain barrier (BBB), enter the brain parenchyma, ingress into individual human brain cells, and autoposition themselves at the axon initial segments of neurons (endoneurobots), within glial cells (gliabots), and in intimate proximity to synapses (synaptobots). They would then wirelessly transmit up to ~6 x 10^16 bits per second of synaptically processed and encoded human–brain electrical information via auxiliary nanorobotic fiber optics (30 cm^3) with the capacity to handle up to 10^18 bits/sec and provide rapid data transfer to a cloud based supercomputer for real-time brain-state monitoring and data extraction. A neuralnanorobotically enabled human B/CI might serve as a personalized conduit, allowing persons to obtain direct, instantaneous access to virtually any facet of cumulative human knowledge. Other anticipated applications include myriad opportunities to improve education, intelligence, entertainment, traveling, and other interactive experiences. A specialized application might be the capacity to engage in fully immersive experiential/sensory experiences, including what is referred to here as “transparent shadowing” (TS). Through TS, individuals might experience episodic segments of the lives of other willing participants (locally or remote) to, hopefully, encourage and inspire improved understanding and tolerance among all members of the human family.

Citation: Martins NRB, Angelica A, Chakravarthy K, Svidinenko Y, Boehm FJ, Opris I, Lebedev MA, Swan M, Garan SA, Rosenfeld JV, Hogg T and Freitas RA Jr (2019) Human Brain/Cloud Interface. Front. Neurosci. 13:112. doi: 10.3389/fnins.2019.00112

Human Connectome Mapping and Monitoring Using Neuronanorobots

Abstract: Neuronanorobotics is the application of medical nanorobots to the human brain. This paper proposes three specific classes of neuronanorobots, named endoneurobots, gliabots and synaptobots, which together can non-destructively map and monitor the structural changes occurring on the 86 x 109 neurons and the 2.42 x 1014 synapses in the human brain, while also recording the synaptic-processed 4.31 x 1015 spikes/sec carrying electrical functional information processed in the neuronal and synaptic network.

Citation: Nuno R. B. Martins, Wolfram Erlhagen, Robert A. Freitas Jr., “Human connectome mapping and monitoring using neuronanorobots”, Journal of Evolution and Technology – Vol. 26 Issue 1 – January 2016 – pgs 1-24.

Action Potential Monitoring Using Neuronanorobots: Neuroelectric Nanosensors

Abstract: Neuronanorobotics, a key future medical technology that can enable the preservation of human brain information, requires appropriate nanosensors. Action potentials encode the most resource-intensive functional brain data. This paper presents a theoretical design for electrical nanosensors intended for use in neuronanorobots to provide non-destructive, in vivo, continuous, real-time, single-spike monitoring of action potentials initiated and processed within the ~86 × 109 neurons of the human brain as intermediated through the ~2.4 × 1014 human brain synapses. The proposed ~3375 nm3 FET-based neuroelectric nanosensors could detect action potentials with a temporal resolution of at least 0.1 ms, enough for waveform characterization even at the highest human neuron firing rates of 800 Hz.

Citation: Nuno R. B. Martins, Wolfram Erlhagen, Robert A. Freitas Jr., “Action Potential Monitoring Using Neuronanorobots: Neuroelectric Nanosensors”, Intl. J. Nanomaterials and Nanostructures 1(June 2015):20-41.

Non-destructive whole-brain monitoring using nanorobots: Neural electrical data rate requirements

Abstract: Neuronanorobotics, a promising future medical technology, may provide the ultimate tool for achieving comprehensive non-destructive real-time in vivo monitoring of the many information channels in the human brain. This paper focuses on the electrical information channel and employs a novel electrophysiological approach to estimate the data rate requirements, calculated to be (5.52 ± 1.13) x 1016 bits/sec in an entire living human brain, for acquiring, transmitting, and storing single-neuron electrical information using medical nanorobots, corresponding to an estimated synaptic-processed spike rate of (4.31 ± 0.86) x 1015 spikes/sec.

Citation: Nuno R. B. Martins, Wolfram Erlhagen, Robert A. Freitas Jr., “Non-destructive whole-brain monitoring using nanorobots: Neural electrical data rate requirements”, Intl. J. Machine Consciousness 4(June 2012):109-140.

A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus

Abstract: Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-a), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology.

Citation: Talaei K, Garan SA, Quintela BdM, Olufsen MS, Cho J, Jahansooz JR, Bhullar PK, Suen EK, Piszker WJ, Martins NRB, Moreira de Paula MA, dos Santos RW and Lobosco M (2021) A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus. Front. Cell. Infect. Microbiol. 11:711153. doi: 10.3389/fcimb.2021.711153