• Corpus ID: 209832554

A single layer artificial neural network with engineered bacteria

@article{Sarkar2020ASL,
  title={A single layer artificial neural network with engineered bacteria},
  author={Kathakali Sarkar and Deepro Bonnerjee and Sangram Bagh},
  journal={ArXiv},
  year={2020},
  volume={abs/2001.00792}
}
The abstract mathematical rules of artificial neural network (ANN) are implemented through computation using electronic computers, photonics and in-vitro DNA computation. Here we demonstrate the physical realization of ANN in living bacterial cells. We created a single layer ANN using engineered bacteria, where a single bacterium works as an artificial neuron and demonstrated a 2-to-4 decoder and a 1-to-2 de-multiplexer for processing chemical signals. The inputs were extracellular chemical… 
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References

SHOWING 1-8 OF 8 REFERENCES

Neural network computation with DNA strand displacement cascades

It is suggested that DNA strand displacement cascades could be used to endow autonomous chemical systems with the capability of recognizing patterns of molecular events, making decisions and responding to the environment.

Towards Multicellular Biological Deep Neural Nets Based on Transcriptional Regulation

An architecture for constructing multicellular neural networks and programmable nonlinear systems is proposed and an artificial neuron based on gene regulatory networks is designed and optimize its dynamics for modularity.

Artificial neural networks enabled by nanophotonics

Research into emerging ANNs enabled by nanophtonics that harness photons’ ability to carry vast amounts of information that will help researchers develop artificial neural networks with uses including brain disease research and machine learning are reviewed.

Neural Model of the Genetic Network*

The comparison proves that the neural network model describes behavior of the system in full agreement with experiments; moreover, it predicts its function in experimentally inaccessible situations and explains the experimental observations.

Synthetic genetic circuits for programmable biological functionalities.

A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains

  • A. Zador
  • Computer Science, Biology
    bioRxiv
  • 2019
It is argued that much of an animal’s behavioral repertoire is not the result of clever learning algorithms—supervised or unsupervised—but arises instead from behavior programs already present at birth, which arise through evolution, are encoded in the genome, and emerge as a consequence of wiring up the brain.

A frame-shifted gene, which rescued its function by non-natural start codons and its application in constructing synthetic gene circuits

It is reported that the function of gene circuits is rescued by a frame-shifted gene, which functions by translating from a non-natural start codon, which serves as a novel way of building and optimizing synthetic-gene-circuits.

Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements.

Controlling the expression of the genes encoding luciferase, the low abundance E.coli protein DnaJ and restriction endonuclease Cfr9I not only demonstrates that high levels of expression can be achieved but also suggests that under conditions of optimal repression only around one mRNA every 3rd generation is produced.