Extended Data Fig. 3: Experimental characterization of winner-take-all DNA neural networks.
From: Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks
a, Two-species winner-take-all behaviour. The experimental data (left, same as Fig. 2a) were used to identify the reverse rate constant kr = 0.4 s−1 of the annihilation reaction in simulations (right). All fluorescence kinetics data and simulation are shown over the course of 2.5 h. The standard concentration is 50 nM (1×). Initial concentrations of the annihilator, restoration gates, fuels and reporters are 75 nM (1.5×), 50 nM (1×), 100 nM (2×) and 100 nM (2×), respectively. b, A 4-bit pattern recognition circuit with input concentration varying from 50 nM to 500 nM. In each output trajectory plot, dotted lines indicate fluorescence kinetics data and solid lines indicate simulation. The patterns to the left and right of the arrow indicate input signal and output classification, respectively. c, Applying thresholding to clean up noisy input signals. The thresholding mechanism has been reported previously in work on seesaw DNA circuits11. The extended toehold in threshold molecule has 7 nucleotides. In b and c, to compare the range of inputs, the concentration of each input strand is shown relative to 50 nM. The initial concentration of each weight molecule is either 0 or 50 nM; weight fuels are twice the concentration of weight molecules. The initial concentrations of the summation gates, annihilator, restoration gates, restoration fuels and reporters are 100 nM (1×), 400 nM (4×), 100 nM (1×), 200 nM (2×) and 200 nM (2×), respectively, with a standard concentration of 100 nM.
