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.
Highlights
The connections between neurons determine the computations performed by both artificial and biological neural networks
We formulate a different problem: Given an ensemble of connections represented by barcode pairs, can we copy them into a new network? In other words, can the original network be cloned? We explore a computational model that simulates the behavior of barcodes introduced into a tabula rasa network with unstructured connectivity and test its ability to recreate target connectivity in such networks
On their origin, the connections can be represented by an ensemble of DNA barcode pairs (3, 4)
Summary
The connections between neurons determine the computations performed by both artificial and biological neural networks. 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. The connections between neurons determine the computations performed by a neural network In both biological and artificial neural networks, connections are established and tuned by experience and learning. Most current implementations of artificial neural networks exploit digital computers and graphics processing units (2) On these architectures, connections are stored explicitly and are straightforward to extract and copy into a new network. To convert neuronal connectivity into a sequencing problem, we induce individual neurons to express unique random nucleotide identifiers called “barcodes.” Pairs of presynaptic and postsynaptic barcodes represent individual synaptic connections. We present an algorithm that allows robust copying of connectivity based only on local interactions
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