Extended Data Fig. 4: A winner-take-all DNA neural network that recognizes 9-bit patterns as ‘L’ or ‘T’.
From: Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks
In each output trajectory plot, dotted lines indicate fluorescence kinetics data and solid lines indicate simulation. The standard concentration is 50 nM (1×). The initial concentration of each input strand is either 0 or 50 nM (1×). The initial concentration of each weight molecule is either 0 or 10 nM (0.2×); weight fuels are twice the concentration of weight molecules. The initial concentrations of the summation gates, annihilator, restoration gates, restoration fuels and reporters are 50 nM (1×), 75 nM (1.5×), 50 nM (1×), 100 nM (2×) and 100 nM (2×), respectively. The patterns to the left and right of the arrow indicate input signal and output classification, respectively. In addition to the perfect inputs, 28 example input patterns with 1–5 corrupted bits were tested. Note that 5 is the maximum number of corrupted bits, because an ‘L’ with more than 5-bit corruption will be as similar as or more similar to a ‘T’, and vice versa.
