Section: New Results

Reconfigurable Neuromorphic Computation in Biochemical Systems

Participants : Katherine Chiang, François Fages.

Implementing application-specific computation and con- trol tasks within a biochemical system has been an important pursuit in synthetic biology. Most synthetic designs to date have focused on realiz- ing systems of fixed functions using specifically engineered components, thus lacking flexibility to adapt to uncertain and dynamically-changing environments. To remedy this limitation, an analog and modularized approach to realize reconfigurable neuromorphic computation with biochemical reactions is presented in [11] . We propose a biochemical neural network consisting of neuronal modules and interconnects that are both reconfigurable through external or internal control over the concentrations of certain molecular species. Case studies on clas- sification and machine learning applications using the DNA strain displacement technology demonstrate the effectiveness of our design in both reconfiguration and autonomous adaptation.