Physical Principles for Scalable Neural Recording

The following is an analysis of what would be needed to have access to most events of interest in the brain. 

Physical Principles for Scalable Neural Recording by Adam H. Marblestone, Bradley M. Zamft, Yael G. Maguire, Mikhail G. Shapiro, Thaddeus R. Cybulski, Joshua I. Glaser, Ben Stranges, Reza Kalhor, David A. Dalrymple, Dongjin Seo, Elad Alon, Michel M. Maharbiz,Jose Carmena, Jan Rabaey, Edward S. Boyden, George M. Church, Konrad P. Kording

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. We also study the physics of powering and communicating with microscale devices embedded in brain tissue.

I note the following:

While optics might seem to require a number of photodetectors, fibers or waveguide ports comparable to the number of neurons, new developments suggest ways of imaging with fewer elements. For example, compressive sensing or ghost imaging techniques based on random mask projections [20, 38, 57, 100] might allow a smaller number of photodetectors to be used. In an illustrative case, an imaging system may be constructed simply from a single photodetector and a transmissive LCD screen presenting a series of random binary mask patterns [12], where the number of required mask patterns is much smaller than the number of image pixels due to a compressive reconstruction. Furthermore, it is possible to directly image through gradient index of refraction (GRIN) lenses [34] or optical fibers [13, 65, 103], thus multiplexing multiple observed neurons per fiber.

Yes. mutliplexing would be good :-) and reading this chart,

I am thinking there might be other good reasons as to why you'd want compressive sensing related techniques. More on that later.

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