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      Physical principles for scalable neural recording

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          Abstract

          Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.

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          Chronically implanted recording electrode arrays linked to prosthetics have the potential to make positive impacts on patients suffering from full or partial paralysis. Such arrays are implanted into the patient's cortical tissue and record extracellular potentials from nearby neurons, allowing the information encoded by the neuronal discharges to control external devices. While such systems perform well during acute recordings, they often fail to function reliably in clinically relevant chronic settings. Available evidence suggests that a major failure mode of electrode arrays is the brain tissue reaction against these implants, making the biocompatibility of implanted electrodes a primary concern in device design. This review presents the biological components and time course of the acute and chronic tissue reaction in brain tissue, analyses the brain tissue response of current electrode systems, and comments on the various material science and bioactive strategies undertaken by electrode designers to enhance electrode performance.
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            Large-scale recording of neuronal ensembles.

            How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron-electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
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                Author and article information

                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                21 October 2013
                2013
                : 7
                : 137
                Affiliations
                [1] 1Biophysics Program, Harvard University Boston, MA, USA
                [2] 2Wyss Institute for Biologically Inspired Engineering at Harvard University Boston, MA, USA
                [3] 3Department of Genetics, Harvard Medical School Boston, MA, USA
                [4] 4Plum Labs LLC Cambridge, MA, USA
                [5] 5Division of Chemistry and Chemical Engineering, California Institute of Technology Pasadena, CA, USA
                [6] 6Interdepartmental Neuroscience Program, Northwestern University Chicago, IL, USA
                [7] 7Department of Radiology, Stanford University Palo Alto, CA, USA
                [8] 8Nemaload San Francisco, CA, USA
                [9] 9Media Laboratory, Massachusetts Institute of Technology Cambridge, MA, USA
                [10] 10Department of Electrical Engineering and Computer Sciences, University of California at Berkeley Berkeley, CA, USA
                [11] 11Helen Wills Neuroscience Institute, University of California at Berkeley Berkeley, CA, USA
                [12] 12Departments of Brain and Cognitive Sciences and Biological Engineering, Massachusetts Institute of Technology Cambridge, MA, USA
                [13] 13Departments of Physical Medicine and Rehabilitation and of Physiology, Northwestern University Feinberg School of Medicine Chicago, IL, USA
                [14] 14Sensory Motor Performance Program, The Rehabilitation Institute of Chicago Chicago, IL, USA
                Author notes

                Edited by: David Hansel, University of Paris, France

                Reviewed by: Simon R. Schultz, Imperial College London, UK; Venkatesh N. Murthy, Harvard University, USA

                *Correspondence: Adam H. Marblestone, Biophysics Program, Harvard University and Wyss Institute, CLSB 530/4D, 3 Blackfan Circle, Boston, MA 02115 USA e-mail: adam.h.marblestone@ 123456gmail.com

                This article was submitted to the journal Frontiers in Computational Neuroscience.

                †Joint first authors.

                ‡Joint last authors.

                Article
                10.3389/fncom.2013.00137
                3807567
                24187539
                180600de-a100-42c5-a48e-6ff03ad1e846
                Copyright © 2013 Marblestone, Zamft, Maguire, Shapiro, Cybulski, Glaser, Amodei, Stranges, Kalhor, Dalrymple, Seo, Alon, Maharbiz, Carmena, Rabaey, Boyden, Church and Kording.

                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) or licensor 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
                : 07 July 2013
                : 23 September 2013
                Page count
                Figures: 6, Tables: 1, Equations: 36, References: 345, Pages: 34, Words: 32396
                Categories
                Neuroscience
                Hypothesis and Theory Article

                Neurosciences
                neural recording,brain activity mapping,electrical recording,optical methods,magnetic resonance imaging,molecular recording,embedded electronics

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