Towards a Biomolecular Learning Machine
Learning and generalisation are fundamental behavioural traits of intelligent life. We present a synthetic biochemical circuit which can exhibit non-trivial learning and generalisation behaviours, which is a first step towards demonstrating that these behaviours may be realised at the molecular level. The aim of our system is to learn positive real-valued weights for a real-valued linear function of positive inputs. Mathematically, this can be viewed as solving a non-negative least-squares regression problem. Our design is based on deoxyribozymes, which are catalytic DNA strands. We present simulation results which demonstrate that the system can converge towards a desired set of weights after a number of training instances are provided.
KeywordsLogic Gate Training Sequence Training Instance Substrate Molecule Computational Element
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