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. 2023 May;10(14):e2207023.
doi: 10.1002/advs.202207023. Epub 2023 Mar 19.

A Biologically Interfaced Evolvable Organic Pattern Classifier

Jennifer Y Gerasimov  1 , Deyu Tu  1 , Vivek Hitaishi  1 , Padinhare Cholakkal Harikesh  1 , Chi-Yuan Yang  1 , Tobias Abrahamsson  1 , Meysam Rad  1 , Mary J Donahue  1 , Malin Silverå Ejneby  2 , Magnus Berggren  1 , Robert Forchheimer  3 , Simone Fabiano  1
Affiliations

A Biologically Interfaced Evolvable Organic Pattern Classifier

Jennifer Y Gerasimov et al. Adv Sci (Weinh). 2023 May.

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.

Keywords: conducting polymers; electropolymerization; evolvable electronics; neuromorphic hardware; organic electrochemical transistors; organic electronics; synaptic transistors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Training concept. a) Schematic of the simplest circuit producing the weighted sum of n input voltage values and implementing a thresholding operation using the control voltage V c such that the output above the threshold produces positive values and the output below the threshold voltage produces negative values. b) Schematic of a pixel where each weight is split up into a positive and a negative weight coefficient. c) All possible reading modes for a single pixel. d) Translation of the reading scheme to a single‐layer perceptron, e) when a pattern is written positive on a fresh array, each pixel contributes a positive current value to the output. f) Layout of the classifier used in this study.
Figure 2
Figure 2
Device characterization. a) Schematic of a single EOECT. b) Transfer characteristics of a representative EOECT. c) The time‐dependent current response of six independent devices grown by applying a sequence of one‐second electropolymerization pulses (0.5 V) and characterized between pulses at a voltage of −0.2 V. A variable number of pulses, ranging from 2 to 20, was applied to each device. d) Electrical (plot) and visual (inset) characterization of the final properties of the six devices fabricated in panel (c). The colors correspond to the legend in panel (c). e) Current of 32 independent transistors on a single device in response to a 50 mV characterization pulse. f) Stability plots of dry, pre‐fabricated devices over a wide conductance range.
Figure 3
Figure 3
Training the array. a) The output currents of an array during training in response to a given input pattern (x‐axis). The array was initialized using a random set of weights and trained before each characterization phase to produce a positive output for the “T” input pattern and a negative output for a “J” input pattern using the patterns in the grey blocks. The average and standard deviation represent data from four successive read operations. Training in panel (a) was conducted using a microcontroller. b) Training and c) characterization of the training performed using a touchpad.
Figure 4
Figure 4
Pattern classification with a biological actuator. a) Schematic of the connection between the classifier circuit, artificial neuron, and leech. b) Output current of the pre‐trained classifier to a T input and c) a J input. d) Output voltage from the artificial neuron in response to a T input and e) a J input. f) Output voltage from the artificial neuron in response to a sequence of five T and five J inputs (grey) and a simultaneously acquired estimate of leech motion (black).
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