Team:SEU/Improve

Improve

This is our first time to participate in iGEM. According to our survey, there is few similar projects in previous iGEM competition hence our focus in this project lies in improving state-of-the-art molecular neural networks in literature [1].

In [1], the values of input signals of neural networks are binary i.e. the values are constrained to be either 0 or 1, which limits the application in real world. In computer science applications, data are usually real numbers. In addition, training, which is one critical part of artificial intelligence systems, has not been integrated to the molecular system in [1]. Therefore we make such improvements:

1. We support continuous value computation in our molecular systems. CPU/GPU-based software applications can be possibly mapped to our system based on this feature. There is a pattern recognition example in Demonstrate Page.

2. We integrate training to our neural networks. Therefore our system does not require in silico training processes.

Also, we develop a Software Toolto help other researchers design their own systems.

Reference

[1] K. Cherry, L. Qian, "Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks," Nature, vol. 559, no. 7714, pp.370-376, 2018.