AI News, Test tube artificial neural network recognizes 'molecular handwriting'

Test tube artificial neural network recognizes 'molecular handwriting'

'Similar to how electronic computers and smart phones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come.'

In this work, we have designed and created biochemical circuits that function like a small network of neurons to classify molecular information substantially more complex than previously possible,' says Qian.

To illustrate the capability of DNA-based neural networks, Qian laboratory graduate student Kevin Cherry chose a task that is a classic challenge for electronic artificial neural networks: recognizing handwriting.

In the work described in the Nature paper, Cherry, who is the first author on the paper, demonstrated that a neural network made out of carefully designed DNA sequences could carry out prescribed chemical reactions to accurately identify 'molecular handwriting.'

'The lack of geometry is not uncommon in natural molecular signatures yet still requires sophisticated biological neural networks to identify them: for example, a mixture of unique odor molecules comprises a smell,' says Qian.

His system theoretically has the capability of classifying over 12,000 handwritten 6s and 7s -- 90 percent of those numbers taken from a database of handwritten numbers used widely for machine learning -- into the two possibilities.

When given an unknown number, this 'smart soup' would undergo a series of reactions and output two fluorescent signals, for example, green and yellow to represent a 5, or green and red to represent a 9.

Test Tube Artificial Neural Network Recognizes "Molecular Handwriting"

Researchers at Caltech have developed an artificial neural network made out of DNA that can solve a classic machine learning problem: correctly identifying handwritten numbers.

'Similar to how electronic computers and smart phones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come.'

In this work, we have designed and created biochemical circuits that function like a small network of neurons to classify molecular information substantially more complex than previously possible,' says Qian.

In the work described in the Nature paper, Cherry, who is the first author on the paper, demonstrated that a neural network made out of carefully designed DNA sequences could carry out prescribed chemical reactions to accurately identify 'molecular handwriting.'

'The lack of geometry is not uncommon in natural molecular signatures yet still requires sophisticated biological neural networks to identify them: for example, a mixture of unique odor molecules comprises a smell,' says Qian.

His system theoretically has the capability of classifying over 12,000 handwritten 6s and 7s—90 percent of those numbers taken from a database of handwritten numbers used widely for machine learning—into the two possibilities.

The winning competitor is then restored to a high concentration and produces a fluorescent signal indicating the networks' decision.'  Next, Cherry built upon the principles of his first DNA neural network to develop one even more complex, one that could classify single digit numbers 1 through 9.

When given an unknown number, this 'smart soup' would undergo a series of reactions and output two fluorescent signals, for example, green and yellow to represent a 5, or green and red to represent a 9.

DNA-based neural network reads ‘molecular handwriting’

The work is part of an ambitious project to build artificial neural networks – machine learning systems which loosely mimic the behaviour of the biological brain – using DNA.

“Similar to how electronic computers and smart phones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come.” Now, Qian and her colleagues have created DNA-based networks which function like small networks of neurons in order to process molecular information.

This is challenging – often for human as well as computers – due to the enormous range of approaches people take to drawing letters and numbers, and requires a neural network not just to recognise figures, but also to account for variation when it compares a scribbled figure with its “memory” of figures.

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

From bacteria following simple chemical gradients1 to the brain distinguishing complex odour information2, the ability to recognize molecular patterns is essential for biological organisms.

Compared to the linear-threshold circuits7 and Hopfield networks8 used previously3, winner-take-all circuits are computationally more powerful4, 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.

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.

Boffins build neural networks fashioned out of DNA molecules

Scientists have built neural networks from DNA molecules that can recognise handwritten numbers, a common task in deep learning, according to a paper published in Nature on Wednesday.

“In this work, we have designed and created biochemical circuits that function like a small network of neurons to classify molecular information substantially more complex than previously possible.'

Each pattern is made up of 20 distinct DNA molecules that trace out a number from one to nine chosen from a set of 100 that represent the 100 bits in each 10 x 10 grid.

Once grabbed onto the tail, it can force the nucleotides in the double strands to open up, one nucleotide at a time, until the previously bound strand is released,' Qian explained to The Register.

Once released, the output strand can then take on a different role as an input to interact with yet another double-stranded DNA molecule, leading to a network of molecular interactions that compute more complex input-output functions.'

Realistically, based on simulations and our understanding of the DNA molecules’ performance, we believe the network is capable of classifying 90% of all ‘6’ and ‘7’ digits in the MNIST database,' Kevin Cherry, first author of the paper and graduate student at Caltech, told El Reg.

'For example, one day a DNA-based neural network may be used to detect a patient’s blood glucose level, or a number of other molecules, and immediately respond by releasing an appropriate amount of insulin – all without human intervention,' Cherry said.

In World First, Researchers Create AI From Test Tube DNA

A team of researchers have developed an artificial neural network made out of DNA that is capable of solving a classic machine learning problem: correctly identifying handwritten numbers.

Pioneered by Geoffrey Hinton, artificial neural networks are modelled on the human brain, functioning like a network of neurons capable of processing complex information through a technique known as ‘backwashing’

“Similar to how electronic computers and smart phones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come.”

“The lack of geometry is not uncommon in natural molecular signatures yet still requires sophisticated biological neural networks to identify them: for example, a mixture of unique odor molecules comprises a smell.”

Boffins build neural networks fashioned out of DNA molecules

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