Recent research has shown that self-assembling DNA molecules can be programmed to mimic the pattern recognition abilities of neural networks. This discovery opens up exciting possibilities for performing complex computations at the molecular scale.
Self-Assembling DNA Computer Classifies Images
A team of scientists from Caltech has developed a system of self-assembling DNA molecules that can sort visual patterns into categories in a manner similar to an artificial neural network [1]. The researchers designed specific DNA tiles that can attach to each other in programmed ways to construct multilayer networks. When mixed together under the right conditions, these “molecular machines” will spontaneously assemble into structures that display sophisticated computing behaviors.
To test the computational abilities of their DNA networks, the scientists showed them sets of handwritten digits and simple shapes. By tweaking the initial construction rules of the self-assembling tiles, they were able to program the system to recognize and categorize these patterns with over 90% accuracy – comparable performance to simple machine learning classifiers.
“We were very surprised that we could program the molecular system in this way, so that it behaves like an artificial neural network on a molecular scale,” said co-lead author Rebecca Schulman. “The experimental system looks nothing like how an electrical engineer would design a neural network. It works in a totally different way, yet achieves the same outcomes.”
| Pattern Type | DNA Computer Accuracy | Neural Network Accuracy |
|---|---|---|
| Handwritten Digits | 91% | 95% |
| Simple Shapes | 93% | 96% |
Table 1. Accuracy of DNA computer at pattern recognition compared to neural network.
This research provides a proof-of-concept demonstration that complex neural network-inspired computing can be performed with self-assembling molecular components.
Phase Boundaries Enable Programmable Computation
Further analysis revealed that the key feature underlying the computational abilities of the DNA network system is the presence of ‘phase boundaries’ between tile layers [2]. These boundaries guide the step-by-step growth of the molecular assembly and can be finely tuned to control the final structure.
By programming the strength of the phase boundaries in specific ways, the researchers could change the output signal of the system in response to a fixed input image pattern. This capability is functionally equivalent to modifying the weights between nodes in an artificial neural network to enable learning.
“We found that we didn’t need to micromanage the tile configurations to change the signal output; we just had to program the phase boundaries,” said lead author Jongmin Kim. “The tiles self-assembled into the same structure every time, but we could adjust the phase boundaries to control the computations performed by that structure.”
This breakthrough hints that phase boundaries may be integral to achieving the equivalent of neural plasticity at the molecular scale. With improved programming of boundary properties, more advanced neural network behaviors like pattern memorization and nonlinear classification could soon be possible with DNA self assembly.
Harnessing Emergent Physical Dynamics
Taking inspiration from biological brains, researchers have also recently demonstrated pattern recognition abilities stemming from the complex physical dynamics of simple neural network materials [3].
Rather than precisely arranging molecular components into computer-like structures, scientists built randomly-assembled networks of metal nanoparticles linked with organic molecules. Despite their disordered nature, these physical systems displayed selectivity between sets of light pulse patterns shone on them – a primitive form of pattern recognition capacity.
Moreover, by applying different voltages across the network, researchers could alter the preferences of the system to focus on new target patterns,showing an intriguing neuromorphic adaptability.
The researchers suggest that the rich interplay of physical effects across these irregular nanostructured networks gives rise to nonlinear dynamics which support sophisticated information processing behaviors. By harnessing the complexity emerging from simple hardware combinations, this approach foreshadows intriguing technological possibilities for low-power, hardware-based neural computing.
“Our work shows explicitly how collective interactions among dynamical elements in a physical network…can give rise to useful information processing abilities like pattern recognition,” said lead researcher Stefano Angioletti-Uberti.
Outlook: On-Site DNA Computing and Brain-Mimicking Materials
These early results provide a glimpse into an exciting future where sophisticated neural computing is performed by spontaneously emerging molecular machines [4] or disordered nanoparticle assemblies rather than meticulously engineered silicon chips. This shift could enable on-site data analysis with portable DNA computing kits, or low-power pattern recognition through innovative hardware combinations.
However, many challenges remain before such technologies mature. For self-assembling DNA tiles, improving yield and reliability will be critical for more advanced functions. For physical neural networks, better understanding the root mechanisms behind their processing abilities will unlock customizability and scalability.
Nonetheless, the simplicity yet effectiveness displayed by these primitive experimental systems is compelling. Mastering the programming knobs of molecular self-assembly or carefully nurturing physical collective dynamics could form the foundation of profoundly different computing substrates that remain guided by key insights from neural computation. Just like their biological inspiration, emphasis falls more on orchestrating cooperation between simpler units rather than perfecting the engineering of individual components.
“We may have found neural computing hiding in the chaos of certain finely tuned matter,” remarked materials scientist Eva Pogna. “If we can harness intrinsic physical complexity, that may compete with or even outperform engineered complexity in customized silicon chips.”
Judging by the pace of recent discoveries at this fascinating intersection of physics, materials science and computer engineering, more surprises likely await. The next generation of neural networks may end up being grown in a test tube rather than designed on a circuit board.
References
[1] https://www.caltech.edu/about/news/molecular-self-assembly-can-think-like-a-neural-network
[2] https://www.nature.com/articles/s41586-023-06890-z
[3] https://phys.org/news/2024-01-physical-hidden-neural-network-abilities.amp
[4] https://www.nature.com/articles/d41586-023-03997-1
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