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The Creation of Neural Networks with Artificial Intelligence

By: Sai Srihaas Potu

In recent years, artificial neural networks (ANNs) have become a popular and helpful model for classification, clustering, pattern recognition, and prediction in many disciplines. ANNs are one type of model for machine learning. Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve the delivery of care at a reduced cost.


As health care systems in developed countries transform towards a value-based, patient-centered model of care delivery, we face new complexities relating to improving the structure and management of health care delivery. Artificial intelligence lies at the nexus of new technologies with the potential to deliver health care that is cost-effective and address non-traditional care settings. With the rapid uptake of artificial intelligence to make increasingly complex decisions across different industries, there are a multitude of solutions capable of addressing these health care management challenges.


Artificial intelligence has been the inspiration for countless books and movies, as well as the aspiration of countless scientists and engineers. Researchers at the California Institute of Technology have now taken a major step toward creating artificial intelligence—not in a robot or a silicon chip, but a test tube. The researchers are the first to have made an artificial neural network out of DNA, creating a circuit of interacting molecules that can recall memories based on incomplete patterns.


Consisting of four artificial neurons made from 112 distinct DNA strands, the researchers’ neural network plays a mind-reading game in which it tries to identify a mystery scientist. The researchers trained the neural network to know four scientists, whose identities are each represented by a unique set of answers to four yes-or-no questions, such as whether the scientist was British.


After thinking of a scientist, a human player provides an incomplete subset of answers that partially identifies the scientist. The player then conveys those clues to the network by dropping DNA strands that correspond to those answers into the test tube. Communicating via fluorescent signals, the network then identifies which scientist the player has in mind. The network can also say that it has insufficient information to pick just one of the scientists in its memory or that the clues contradict what it has remembered. The researchers played this game with the network using 27 different ways of answering the questions and it correctly responded each time. This DNA-based neural network demonstrates the ability to take an incomplete pattern and figure out what it might represent—one of the brain’s unique features.


Biochemical systems with artificial intelligence—or at least some basic, decision-making capabilities—could have powerful applications in medicine, chemistry, and biological research. In the future, such systems could operate within cells, helping to answer fundamental biological questions or diagnose a disease. Biochemical processes that can intelligently respond to the presence of other molecules could allow engineers to produce increasingly complex chemicals or build new kinds of structures, molecule by molecule.


The researchers based their biochemical neural network on a simple model of a neuron, called a linear threshold function. The model neuron receives input signals, multiplies each by a positive or negative weight, and only if the weighted sum of inputs surpasses a certain threshold does the neuron fire, producing an output. This model is an oversimplification of real neurons, says paper coauthor Erik Winfree, professor of computer science, computation and neural systems, and bioengineering.


To build the DNA neural network, the researchers used a process called a strand-displacement cascade. Previously, the team developed this technique to create the largest and most complex DNA circuit yet, one that computes square roots.


This method uses single and partially double-stranded DNA molecules. The latter are double helices, one strand of which sticks out like a tail. While floating around in a water solution, a single strand can run into a partially double-stranded one, and if their bases are complementary, the single strand will grab the double strand’s tail and bind, kicking off the other strand of the double helix. The single strand thus acts as an input while the displaced strand acts as an output, which can then interact with other molecules.


Because they can synthesize DNA strands with whatever base sequences they want, the researchers can program these interactions to behave like a network of model neurons. By tuning the concentrations of every DNA strand in the network, the researchers can teach it to remember the unique patterns of yes-or-no answers that belong to each of the four scientists. Unlike some artificial neural networks that can directly learn from examples, the researchers used computer simulations to determine the molecular concentration levels needed to implant memories into the DNA neural network.


While this proof-of-principle experiment shows the promise of creating DNA-based networks that can think, this neural network is limited, the researchers say. The human brain consists of 100 billion neurons but creating a network with just 40 of these DNA-based neurons—ten times larger than the demonstrated network—would be a challenge, according to the researchers.


Furthermore, the system is slow; the test-tube network took eight hours to identify each mystery scientist. The molecules are also used up—unable to detach and pair up with a different strand of DNA—after completing their task, so the game can only be played once. Perhaps in the future, a biochemical neural network could learn to improve its performance after many repeated games or learn new memories from encountering new situations. Creating biochemical neural networks that operate inside the body—or even just inside a cell on a Petri dish—is also a long way away, since making this technology work in a lab poses an entirely different set of challenges.


ANN is a new computational model with rapid and large uses for handling various complex real-world issues. ANNs popularity lies in information processing characteristics to learning power, high parallelism, noise tolerance, and capabilities of generalization. Successful implementation and adoption may require an improved understanding of the ethical, societal, and economic implications of applying ANNs into real-life situations.


References:

1. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: a survey. Heliyon. 2018.

2. Lulu Qian, Erik Winfree, Jehoshua Bruck. Neural network computation with DNA strand displacement cascades. Nature. 2011.

3. Paliwal M, Kumar U. Neural networks and statistical techniques: A review of applications. Expert Systems with Applications. 2009.

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