Design of a biochemical circuit motif for learning linear functions

Lakin, Matthew R.; Minnich, Amanda; Lane, Terran; Stefanović, Darko

doi:10.1098/rsif.2014.0902

Cited by 15 publications

(13 citation statements)

References 76 publications

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“…McGregor et al [42] demonstrated how in silico evolution can be used used to develop artificial chemistries capable of learning some particular fixed function. Lakin et alʼs scheme for rudimentary learning in an enzymatic chemistry [34] is equivalent to non-negative least-squares regression and achieves learning within a chemical system (and similarly for our more recent learning scheme based on buffered DNA strand displacement chemistry [35,36]). Our FCNN differs from these architectures in two important ways.…”

Section: Related Workmentioning

confidence: 79%

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Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor

et al. 2017

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Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system.

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Section: Related Workmentioning

confidence: 79%

“…Several groups have designed chemical systems based on learning models other than neural networks, such as decision trees [48] and least-squares regression (LSR) [34]. McGregor et al [42] demonstrated how in silico evolution can be used used to develop artificial chemistries capable of learning some particular fixed function.…”

Section: Related Workmentioning

confidence: 99%

Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor

et al. 2017

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“…We have produced circuits that decode broadly relevant but predetermined temporal features. Going forward, it would be interesting to develop molecular circuits that can learn relevant temporal features dynamically − as in machine learning approaches. In the learning paradigm, for example, a molecular circuit could be exposed to two classes of time-varying patterns during a “training phase” (the temporal equivalent of cat and dog images in static pattern recognition).…”

Section: Discussionmentioning

confidence: 99%

Temporal Pattern Recognition through Analog Molecular Computation

O’Brien

Murugan

2019

ACS Synth. Biol.

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Living cells communicate information about physiological conditions by producing signaling molecules in a specific timed manner. Different conditions can result in the same total amount of a signaling molecule, differing only in the pattern of the molecular concentration over time. Such temporally coded information can be completely invisible to even state-of-the-art molecular sensors with high chemical specificity that respond only to the total amount of the signaling molecule. Here, we demonstrate design principles for circuits with temporal specificity, that is, molecular circuits that respond to specific temporal patterns in a molecular concentration. We consider pulsatile patterns in a molecular concentration characterized by three fundamental temporal features -time period, duty fraction and number of pulses. We develop circuits that respond to each one of these features while being insensitive to the others. We demonstrate our design principles using abstract Chemical Reaction Networks and with explicit simulations of DNA strand displacement reactions. In this way, our work develops building blocks for temporal pattern recognition through molecular computation.

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“…With one additional circuit component that actives weight molecules during a supervised training process, the DNA circuits would be capable of activating a specific set of wires in the weight-multiplication layer when exposed to a specific set of patterns. As widely discussed in experimental 27 and theoretical [28][29][30] studies, learning-the most desirable property Fig. 4 | A winner-take-all DNA neural network that recognizes 100-bit patterns as one of nine handwritten digits.…”

Section: Letter Researchmentioning

confidence: 99%

Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks

Cherry

Qian

2018

Nature

402

396

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From bacteria following simple chemical gradients to the brain distinguishing complex odour information, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks, but has been limited to the recognition of a set of no more than four patterns, each composed of four distinct DNA molecules. Winner-take-all computation has been suggested as a potential strategy for enhancing the capability of DNA-based neural networks. Compared to the linear-threshold circuits and Hopfield networks used previously, winner-take-all circuits are computationally more powerful, allow simpler molecular implementation and are not constrained by the number of patterns and their complexity, so both a large number of simple patterns and a small number of complex patterns can be recognized. Here we report a systematic implementation of winner-take-all neural networks based on DNA-strand-displacement reactions. We use a previously developed seesaw DNA gate motif, extended to include a simple and robust component that facilitates the cooperative hybridization that is involved in the process of selecting a 'winner'. We show that with this extended seesaw motif DNA-based neural networks can classify patterns into up to nine categories. Each of these patterns consists of 20 distinct DNA molecules chosen from the set of 100 that represents the 100 bits in 10 × 10 patterns, with the 20 DNA molecules selected tracing one of the handwritten digits '1' to '9'. The network successfully classified test patterns with up to 30 of the 100 bits flipped relative to the digit patterns 'remembered' during training, suggesting that molecular circuits can robustly accomplish the sophisticated task of classifying highly complex and noisy information on the basis of similarity to a memory.

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Design of a biochemical circuit motif for learning linear functions

Cited by 15 publications

References 76 publications

Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor

Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor

Temporal Pattern Recognition through Analog Molecular Computation

Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks

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