A Biologically Interfaced Evolvable Organic Pattern Classifier.

Abstract

Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process these data into meaningful information, and translate that information into a format that can be interpreted by living systems. Here, the first example of interfacing a hardware-based pattern classifier with a biological nerve is reported. The classifier implements the Widrow-Hoff learning algorithm on an array of evolvable organic electrochemical transistors (EOECTs). The EOECTs' channel conductance is modulated in situ by electropolymerizing the semiconductor material within the channel, allowing for low voltage operation, high reproducibility, and an improvement in state retention by two orders of magnitude over state-of-the-art OECT devices. The organic classifier is interfaced with a biological nerve using an organic electrochemical spiking neuron to translate the classifier's output to a simulated action potential. The latter is then used to stimulate muscle contraction selectively based on the input pattern, thus paving the way for the development of adaptive neural interfaces for closed-loop therapeutic systems.