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      Human Brain/Cloud Interface

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          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 × 10 9 neurons and ∼2 × 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 × 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.

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          Parallel organization of functionally segregated circuits linking basal ganglia and cortex.

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            The molecular biology of memory storage: a dialogue between genes and synapses.

            E R Kandel (2001)
            One of the most remarkable aspects of an animal's behavior is the ability to modify that behavior by learning, an ability that reaches its highest form in human beings. For me, learning and memory have proven to be endlessly fascinating mental processes because they address one of the fundamental features of human activity: our ability to acquire new ideas from experience and to retain these ideas over time in memory. Moreover, unlike other mental processes such as thought, language, and consciousness, learning seemed from the outset to be readily accessible to cellular and molecular analysis. I, therefore, have been curious to know: What changes in the brain when we learn? And, once something is learned, how is that information retained in the brain? I have tried to address these questions through a reductionist approach that would allow me to investigate elementary forms of learning and memory at a cellular molecular level-as specific molecular activities within identified nerve cells.
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              The importance of mixed selectivity in complex cognitive tasks.

              Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input-output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                29 March 2019
                2019
                : 13
                : 112
                Affiliations
                [1] 1Lawrence Berkeley National Laboratory , Berkeley, CA, United States
                [2] 2Center for Research and Education on Aging (CREA), University of California, Berkeley and LBNL , Berkeley, CA, United States
                [3] 3Kurzweil Technologies , Newton, MA, United States
                [4] 4UC San Diego Health Science , San Diego, CA, United States
                [5] 5VA San Diego Healthcare System , San Diego, CA, United States
                [6] 6Nanobot Medical Animation Studio , San Diego, CA, United States
                [7] 7NanoApps Medical, Inc. , Vancouver, BC, Canada
                [8] 8Miami Project to Cure Paralysis, University of Miami , Miami, FL, United States
                [9] 9Department of Biomedical Engineering, University of Miami , Coral Gables, FL, United States
                [10] 10Center for Neuroengineering, Duke University , Durham, NC, United States
                [11] 11Center for Bioelectric Interfaces of the Institute for Cognitive Neuroscience of the National Research University Higher School of Economics , Moscow, Russia
                [12] 12Department of Information and Internet Technologies of Digital Health Institute, I.M. Sechenov First Moscow State Medical University , Moscow, Russia
                [13] 13Department of Philosophy, Purdue University , West Lafayette, IN, United States
                [14] 14Monash Institute of Medical Engineering, Monash University , Clayton, VIC, Australia
                [15] 15Department of Neurosurgery, Alfred Hospital , Melbourne, VIC, Australia
                [16] 16Department of Surgery, Monash University , Clayton, VIC, Australia
                [17] 17Department of Surgery, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences , Bethesda, MD, United States
                [18] 18Institute for Molecular Manufacturing , Palo Alto, CA, United States
                Author notes

                Edited by: Hari S. Sharma, Uppsala University, Sweden

                Reviewed by: Vassiliy Tsytsarev, University of Maryland, College Park, United States; Brent Winslow, Design Interactive, United States

                *Correspondence: Nuno R. B. Martins, nunomartins@ 123456lbl.gov ; nunorbmartins@ 123456gmail.com

                This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2019.00112
                6450227
                30983948
                e03c4f7c-9b78-44a2-8b22-39c8a2f4a12b
                Copyright © 2019 Martins, Angelica, Chakravarthy, Svidinenko, Boehm, Opris, Lebedev, Swan, Garan, Rosenfeld, Hogg and Freitas.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 September 2018
                : 30 January 2019
                Page count
                Figures: 6, Tables: 2, Equations: 0, References: 274, Pages: 24, Words: 0
                Categories
                Neuroscience
                Hypothesis and Theory

                Neurosciences
                brain/cloud interface,brain-computer interface,brain-to-brain interface,brain-machine interface,transparent shadowing,neuralnanorobots,neuralnanorobotics,nanomedicine

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