Physical principles for scalable neural recording
Shapiro, Mikhail G.
Cybulski, Thaddeus R.
Glaser, Joshua I.
Stranges, P. Benjamin
Dalrymple, David A.
Maharbiz, Michel M.
Carmena, Jose M.
Rabaey, Jan M.
Boyden, Edward S.
Kording, Konrad P.Note: Order does not necessarily reflect citation order of authors.
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CitationMarblestone, A. H., B. M. Zamft, Y. G. Maguire, M. G. Shapiro, T. R. Cybulski, J. I. Glaser, D. Amodei, et al. 2013. “Physical principles for scalable neural recording.” Frontiers in Computational Neuroscience 7 (1): 137. doi:10.3389/fncom.2013.00137. http://dx.doi.org/10.3389/fncom.2013.00137.
AbstractSimultaneously 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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