Shuvaev, S. A., Baserdem, B., Zador, A. M., Koulakov, A. A. (April 2019) Network cloning using DNA barcodes. Proc Natl Acad Sci U S A, 116 (19). pp. 9610-9615. ISSN 0027-8424
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Abstract
The connections between neurons determine the computations performed by both artificial and biological neural networks. Recently, we have proposed SYNSeq, a method for converting the connectivity of a biological network into a form that can exploit the tremendous efficiencies of high-throughput DNA sequencing. In SYNSeq, each neuron is tagged with a random sequence of DNA-a "barcode"-and synapses are represented as barcode pairs. SYNSeq addresses the analysis problem, reducing a network into a suspension of barcode pairs. Here, we formulate a complementary synthesis problem: How can the suspension of barcode pairs be used to "clone" or copy the network back into an uninitialized tabula rasa network? Although this synthesis problem might be expected to be computationally intractable, we find that, surprisingly, this problem can be solved efficiently, using only neuron-local information. We present the "one-barcode-one-cell" (OBOC) algorithm, which forces all barcodes of a given sequence to coalesce into the same neuron, and show that it converges in a number of steps that is a power law of the network size. Rapid and reliable network cloning with single-synapse precision is thus theoretically possible.
| Item Type: | Paper |
|---|---|
| Subjects: | organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks organs, tissues, organelles, cell types and functions organs, tissues, organelles, cell types and functions > sub-cellular tissues: types and functions organs, tissues, organelles, cell types and functions > sub-cellular tissues: types and functions > synapse |
| CSHL Authors: | |
| Communities: | CSHL labs > Koulakov lab CSHL labs > Zador lab |
| Depositing User: | Matthew Dunn |
| Date: | 24 April 2019 |
| Date Deposited: | 22 May 2019 16:59 |
| Last Modified: | 02 Feb 2024 20:44 |
| PMCID: | PMC6511037 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/37831 |
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