DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.040
Publication date: 9th January 2023
Research advancement in brain-machine interfaces, implantable/wearable devices, prosthetics, and intelligent soft robotics calls for close interaction between technology and nature. Since the fundamental building elements of life differ significantly from those utilized in electronic devices, the ability to link an artificial device with a biological system is crucial to the success of these domains. Neuromorphic systems that borrow design concepts from biological systems promise to bridge this gap. Although several software-based neuromorphic algorithms have been integrated into biomedical systems, hardware-based systems that can directly interface with living tissue, adapt based on biological feedback, and utilise event-based sensing and processing capabilities of biological systems are ultimately necessary. However, circuits and devices made of silicon (Si), commonly used in hardware neural networks and neural interfaces, suffer from several drawbacks such as rigidity, lack of biocompatibility, the requirement for numerous circuit elements, and operation mechanisms that are fundamentally different from biological systems, making bio-integration difficult. Hence in this work, I will present Organic Electrochemical transistor-based artificial neurons which can be modulated by ions and exhibit capability for bio integration and feedback. Two diffferent neuron models will be discussed – a simple leaky integrate and fire (LIF) neuron and a conductance-based biorealistic neuron. While the LIF neuon is based on a complementary organic electrochemical transistor-based circuit, the biorealistic neuron utilises antiambipolar behaviour in ladder-type poly(benzimidazobenzophenanthroline) (BBL). The biorealistic neuron can spike at frequencies nearing 100 Hz, emulate most critical neural features, demostrate stochastic spiking, and enable neurotransmitter-/amino acid-/ion-based spiking modulation, which is then used to stimulate biological nerves. These combined features are impossible to achieve using previous technologies.
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