DNA-based neural
networks are a type of DNA circuit capable of
molecular pattern recognition tasks. Winner-take-all DNA networks
have been developed to scale up the complexity of molecular pattern
recognition with a simple molecular implementation. This simplicity
was achieved by replacing negative weights in individual neurons with
lateral inhibition and competition across neurons, eliminating the
need for dual-rail representation. Here we introduce a new type of
DNA circuit that is called loser-take-all: an output signal is ON
if and only if the corresponding input has the smallest analog value
among all inputs. We develop a DNA strand-displacement implementation
of loser-take-all circuits that is cascadable without dual-rail representation,
maintaining the simplicity desired for scalability. We characterize
the impact of effective signal concentrations and reaction rates on
the circuit performance, and derive solutions for compensating undesired
signal loss and rate differences. Using these approaches, we successfully
demonstrate a three-input loser-take-all circuit with nine unique
input combinations. Complementary to winner-take-all, loser-take-all
DNA circuits could be used for recognition of molecular patterns based
on their least similarities to a set of memories, allowing classification
decisions for patterns that are extremely noisy. Moreover, the design
principle of loser-take-all could be more generally applied in other
DNA circuit implementations including k-winner-take-all.