The Great Brain Readout:

Here is our strategy for making CS379C a more valuable experience for both the students and our outside participants and invited speakers: We want to get the best students to participate in CS379C. We want to motivate them to come up with the best ideas and generate the most interesting proposals. Some of the students are interested in pursuing careers in science and others want to found a successful startup. These two goals don’t have to be at odds with one another. We’re putting together a stellar group of scientists, engineers and entrepreneurs to suggest topics and provide students with advice on the relevant areas of biology, neuroscience, nanotechnology and industrial-scale computation and information retrieval. If we are the only audience, then the impact of their work will have primarily localized impact, unless they take the initiative to promote their ideas more widely. Why shouldn’t we provide that extended audience and make their project presentations include a pitch to a blue-ribbon panel of scientists, foundation directors and venture capitalists.

We know a lot of players in this space: scientists, engineers, foundation directors, investment firm partners and program managers from NIH and NSF, Salk Institute, Cold Spring Harbor Laboratory, Siemens, Focused Ultrasound Foundation, Janelia Farm Research Campus of HHMI, Google Ventures, plus a host of colleagues from great research universities. Those we can’t tempt to attend “The Great Brain Readout” in person or by VC at Google, might be interested in reviewing a nicely edited video featuring the best pitches as voted by those attending “The Pitch” event. Internally, we can probably get Alan Eustace (Google SVP Engineering), Alfred Spector (Google VP Knowledge), Astro Teller (Captain of Moonshots Google X), Ray Kurzweil (Google Director of Engineering), Bill Maris (Google Ventures), and Krishna Yeshwant (Google Ventures) interested enough to at least look at the video of top-rated pitches. If BAM is funded, I’ll bet NSF and NIH would be interested in taking a close look at the best projects. There might even be a publication or funding in it for the students with the best ideas.

Project Topics:

Here is a preliminary set of topics for CS379C projects along with one or two possible consultants for each topic to help with identifying relevant papers and offering advice. These suggestions are primarily concerned with possible solutions to the “readout” problem — transmitting neural-state information out of the brain, however, we will consider topics relating to the “sensing” problem — how to record neural-state information including membrane potential, protein expression, calcium concentration and their correlates, and the “inference” problem — suppose we were able to record spikes or protein expression levels from millions of locations within a neural tissue at millisecond resolution, how might we make sense of this deluge of data¹:

• Proposal for scaling SEM² based methods [4, 3, 11] to produce detailed connectomic maps to complement Allen Mouse Brain Atlas — Kevin Briggman, Sebastian Seung

• Analysis of proposal in the “Sequencing the Connectome” paper [13, 2] with completion date based on off-the-shelf technology — Tony Zador, Jon Shlens

• Prospects for building brain-wide self-organizing nanoscale communication networks [5, 6, 1] for solving the readout problem — Yael McGuire, Akram Sadek

• Analysis of the bandwidth and frequency-spectrum issues involved in a cellular-radio-model for whole brain readout — Yael Maguire, Chris Uhlik

• Prospects for growing neural networks or brains around a scaffolding implementing fiber optic communication network — Ed Boyden, Greg Corrado

• Analysis of proposal in the “Molecular Ticker Tapes” paper [7, 8, 14] with a focus on alternative methods for addressing the readout problem — Tony Zador, Jon Shlens

• Computational strategy for managing the billions of terabytes of SEM data likely generated by scalable BAM technology — Mike Hawrylycz, Luc Vincent

• Assessment of tissue damage caused by ultrasound scanning plus basic calculations for back-of-the-envelope analyses — Neal Kassell, Arik Hananel

• Photoacoustic imaging with NIRS³ illumination delivered by a relatively sparse invasive network of fibre optic cables — Ed Boyden, Carl Deisseroth

• Automatic registration of tissue samples with a set of standard exemplars spanning the space of natural variations — Mike Hawrylycz, Clay Reid

• Whole brain mapping of Brodmann areas or alternative anatomic and cytoarchitectural landmarks and areas of interest — Larry Swanson, Fritz Sommer

• Proposal for scaling array tomography [10, 12, 9] to cover full gamut of synaptic proteins to complement Allen Mouse Brain Atlas — Stephen Smith, Mark Schnitzer

• Analysis of the sequencing approach with focused ultrasound raster scan with single and multiple beams and reporters — Bruno Madore, Ben Schwartz

Proposal Components:

Your proposal should address each of the following elements:

• What is the problem you are trying to solve? Be specific. Your project must address the readout problem, but you’ll also have to say something about the technology you’re relying on to extract the information you are reading out. Provide references regarding this recording technology. Your project must also address the scaling problem. What is the state-of-the-art perform ace in terms of number of cells recorded from or other relevant metric and how will you improve on it?

• How will you address the problem? Your readout solution will likely involve multiple technologies. Describe how these technologies will be combined and how you will address the interface issues in particular, e.g., a NEMS device multiplexing input from several fluorescent markers. In the case of off-the-shelf components, provide a credible source. If your solution requires modifying an existing component, describe why you think this is possible and in what sort of time frame.

• How much time do you expect this project will take you? Be realistic. By the time you submit your proposal, you should have already decided whether or not you’ll be working with a team and should have met at least once with members of that team. You should have identified any technical experts you will be consulting as well as additional resources you’ll need. Break it down into tasks and estimate the time allocated to each task and when you plan to put in the effort?

Limit your proposal to a maximum of two pages; if you write much less than one page — 11 point type, one inch margins — then you probably haven’t provided enough detail for me to evaluate. Send your proposal to me at tld [at] google [dot] com by the start of class Monday, May 13, 2013. If you want to run your idea by me before then, send a quick sketch by email or catch me after class.

Example Project Proposal:

Here’s a very rough sketch of a reasonable project:

• Problem description: Design a biocompatible nanoscale RF-based reporter operating in the 1-10GHz range and capable of penetrating tissue to a depth of several centimeters. These devices are intended to be distributed throughout the neural tissue and multiplex and transmit input from many local indicators simultaneously. Together the implanted devices will implement a cellular network capable of recording from millions of cells in the host organism.

• Methods and Results: Estimate the maximum number of transmitters one can fit in an adult skull with the receivers on the skull surface. Account for overlap between the transmission frequencies and assess the consequences of power dissipation and cellular damage in neural tissue. Evaluate existing NEMS technology for multiplexing many inputs to achieve practical transmission rate. Investigate deploying FTDM, TDMA, CDMA to optimize available bandwidth.

• Estimated Effort: Break it down into pieces. Building on existing back-of-the-envelope assessments of applying cell-phone-tower-like technology to the readout problem and consulting with RF experts should take about 10 hours spread out over two to three weeks. Multiplexer-interface issues should require an additional 10 hours spread out over one week. Estimating 10 hours for biocompatablity analysis, and generating the final report and presentation.

When I say “reasonable”, I’m making no judgement as to whether such a technology is plausible, only that the time required to carry out the analysis and complete the project is reasonable for a class project.

References

[1]	A. Paul Alivisatos, Anne M. Andrews, Edward S. Boyden, Miyoung Chun, George M. Church, Karl Deisseroth, John P. Donoghue, Scott E. Fraser, Jennifer Lippincott-Schwartz, Loren L. Looger, Sotiris Masmanidis, Paul L. McEuen, Arto V. Nurmikko, Hongkun Park, Darcy S. Peterka, Clay Reid, Michael L. Roukes, Axel Scherer, Mark Schnitzer, Terrence J. Sejnowski, Kenneth L. Shepard, Doris Tsao, Gina Turrigiano, Paul S. Weiss, Chris Xu, Rafael Yuste, and Xiaowei Zhuang. Nanotools for neuroscience and brain activity mapping. ACS Nano, 7(3):1850–1866, 2013.
[2]	A. Paul Alivisatos, Miyoung Chun, George M. Church, Ralph J. Greenspan, Michael L. Roukes, , and Rafael Yuste. The brain activity map project and the challenge of functional connectomics. Neuron, 74, 2012.
[3]	K.L. Briggman and D.D. Bock. Current opinion neurobiology. Volume electron microscopy for neuronal circuit reconstruction, 22:154–61, 2012.
[4]	K.L. Briggman, M. Helmstaedter, and W. Denk. Wiring specificity in the direction-selectivity circuit of the retina. Nature, 471:183–188, 2011.
[5]	Stephen F. Bush. Nanoscale Communication Networks. Nanoscale Science and Engineering. Artech House, 2010.
[6]	Stephen F. Bush. Toward in vivo nanoscale communication networks: utilizing an active network architecture. Frontiers in Computer Science China, 5(3):316–326, 2011.
[7]	George Church and Jay Shendure. Nucleic acid memory device. US Patent 20030228611, 2003.
[8]	Konrad Kording. Of toasters and molecular ticker tapes. PLoS Computational Biology, 7(12):e1002291, 2011.
[9]	Chenxiang Lin, Ralf Jungmann, Andrew M. Leifer, Chao Li, Daniel Levner, George M. Church, William M. Shih, and Peng Yin. Submicrometre geometrically encoded fluorescent barcodes self-assembled from DNA. Nature Chemistry, 4:832–839, 2012.
[10]	Kristina D. Micheva and Stephen J. Smith. Array tomography: A new tool for imaging the molecular architecture and ultrastructure of neural circuits. Neuron, 55(1):25–36, 2007.
[11]	Shawn Mikula, Jonas Binding, and Winfried Denk. Staining and embedding the whole mouse brain for electron microscopy. Nature Methods, 9:1198–1201, 2012.
[12]	Nancy A. O’Rourke, Nicholas C. Weiler, Kristina D. Micheva, and Stephen J. Smith. Deep molecular diversity of mammalian synapses: Why it matters and how to measure it. Nature Review Neuroscience, 10:365–379, 2012.
[13]	Anthony M. Zador, Joshua Dubnau, Hassana K. Oyibo, Huiqing Zhan, Gang Cao, and Ian D. Peikon. Sequencing the connectome. PLoS Biology, 10(10):e1001411, 2012.
[14]	Bradley Michael Zamft, Adam H. Marblestone, Konrad Kording, Daniel Schmidt, Daniel Martin-Alarcon, Keith Tyo, Edward S. Boyden, and George Church. Measuring cation dependent DNA polymerase fidelity landscapes by deep sequencing. PLoS ONE, 7(8):e43876, 2012.