Towards a biomolecular learning machine (abstract)
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.