Difference between revisions of "Team:SEU/Improve"

Line 71: Line 71:
 
                                       <div>
 
                                       <div>
 
                                           <h1>Improve</h1>
 
                                           <h1>Improve</h1>
−
                                            
+
                                           <p>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 page lies in improving state-of-the-art molecular computing neural networks in literature [1].</p>
 +
                                          <p>In the previous papar, inputs to neural networks are binary i.e. the values are either 0 or 1, which may limit the application. Also, more importantly, training is not integrated to the system. Hence we make such improvements:</p>
 +
                                          <p>1. Employ continuous values during computation.</p>
 +
                                          <p>2. Integrate training to our neuron.</p>
 +
                                          <p>Also, we develop a <a href="https://2019.igem.org/Team:SEU/Software"><b>Software Tool</b></a>to help other researchers design their own systems.</p>
 +
                                          <h3>Reference</h3>
 +
                                          <p>[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.</p>
 
                                 </div>
 
                                 </div>
 
                               </div>
 
                               </div>
Line 81: Line 87:
 
         </div>
 
         </div>
 
     </div>
 
     </div>
 +
</div>
 
</div>
 
</div>
 
</html>
 
</html>

Revision as of 02:15, 18 October 2019





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 page lies in improving state-of-the-art molecular computing neural networks in literature [1].

In the previous papar, inputs to neural networks are binary i.e. the values are either 0 or 1, which may limit the application. Also, more importantly, training is not integrated to the system. Hence we make such improvements:

1. Employ continuous values during computation.

2. Integrate training to our neuron.

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.