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Organic electrochemical transistors (OECTs) have shown great potential in bioelectronics to transduce small biological signals for applications such as the electrical recording of excitable cells and assessing cell barrier properties. It is imperative that operating the OECT as a biosensor does not affect the biological system. However, bias voltages applied to channel materials such as the conducting polymer (CP) PEDOT:PSS have been shown to induce the formation of hydrogen peroxide (H2O2) which can disrupt the physiology of cells. In this work, we evaluated the impact of H2O2 formation during OECT operation by comparing an oxygen-sensitive CP (PEDOT:PSS) and an oxygen-stable CP (p(gPyDPP-MeOT2)). While both CPs show high biocompatibility in their non-biased, resting state, we observed large differences during the operation of the electrochemical device. OECTs with PEDOT:PSS produce H2O2 where the H2O2 concentration in the electrolyte depends on the channel area and the time of operation. In comparison, OECTs using the oxygen-stable DPP-based polymer showed no sign of H2O2 formation. Further investigation also revealed how the proliferation rate of neuronal cells directly interfaced with such OECTs was affected by the concentration of H2O2. Our work demonstrated the limitations of oxygen-sensitive OECT channel materials for bioelectronic applications and provides guidance for material design strategies to develop safe bioelectronic devices for real-life applications.

Organic neuromorphic platforms have recently received growing interest for the implementation and integration of artificial and hybrid neuronal networks. Here, achieving closed-loop and learning/training processes as in the human brain is still a major challenge especially exploiting time-dependent biosignalling such as neurotransmitter release. Here, we present an integrated organic platform capable of cooperating with standard silicon technologies, to achieve brain-inspired computing via adaptive synaptic potentiation and depression, in a closed-loop fashion. The microfabricated platform could be interfaced and control a robotic hand which ultimately was able to learn the grasping of differently sized objects, autonomously.

Neuromorphic systems that display synaptic conditioning based on biochemical signaling activity have recently been introduced in the form of artificial synapses that are model devices to develop tissue-interfaced platforms. In this regard, biohybrid synapses promise adaptive neuron-integrated functions. However, these systems suffer from both molecular cross-talk as biological neural circuits signal transmission typically involves more than one neuromodulator, and unstable electronics wirings as complex architectures are required to interface the tissues. Moreover, whilst novel spiking circuits can work as artificial neurons, they only recreate the biological electrical signaling pathway while electrochemical signal transduction is required for inter-neuron communication. As such, artificial chemically-mediated synapses are essential to perform memory/learning computing functions. Herein, an electrochemical neuromorphic organic device (ENODe) working as an artificial synapse that overcomes electrochemical and readout interferences while it emulates two neurotransmitters synaptic weight modulation and their recycling machinery at the synaptic cleft is shown. Neuronal short- and long-term plasticity are replicated by transducing two separate neurotransmitter-mediated chemical signals into reversible and nonreversible variations of PEDOT:PSS conductance. By exploiting the electrochromic properties of PEDOT:PSS, an alternative optical monitoring strategy is introduced which promises stable multidevice readout from complex bio-hybrid interfaces. The platform emulates high-order biological processes such as intrinsic forgetting, memory consolidation, and neurotransmitter co-modulation. These brain-inspired functionalities herald the development of tissue-integrated neuromorphic systems that combine spiking (electrical neurons) and nonspiking (electrochemical synapses) elements, thus envisioning prosthetic bridges for neural engineering and regenerative medicine.

Synaptic plasticity is at the base of learning and memory capabilities of the human brain and lately recent studies reported a strong correlation between synaptic dysfunction and neurodegenerative diseases. Unfortunately, the complexity of the brain and the nervous system prevents the investigation of mechanisms underlying cognitive impairment, and for this reason the possibility to inhibit neurodegeneration at the early stage of disease is still far from being concrete. The impossibility to study neuronal cells in their native environment pushed the neuroelectronic field towards the implementation of biomimetic in vitro platforms which could resemble the main features of biological synapses, like geometrical shape, structure and functionalities. At this purpose, in recent years supported lipid bilayers and 3D patterned electrodes emerged as promising strategies to mimic neuronal membrane composition and dendritic spines shapes. Additionally, the advent of neuromorphic devices based on conductive polymers provided artificial synapses exhibiting short and long-term plasticity, while being able to transduce biological ionic signals into electrical currents. In this thesis, we present the implementation of biohybrid biomimetic synapses which could pave the way for a new class of adaptable in vitro platforms able to trick cells to recognize electronic devices as part of their native environment. The first part of the project was focused on engineering a biohybrid synapse where a PEDOT:PSS-based OECT acts as artificial post-synaptic neuron while cells directly interfaced with the device represent the biological pre-synaptic end. Of note, the OECT conductance can be modulated by the oxidation of dopamine directly secreted from cells, demonstrating neurotransmitter-mediated short and long-term plasticity. In the second part of the project, the OECT was coupled with a synthetic phospholipid bilayer to implement an artificial synapse with biomimetic features. Finally, the ro

Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems1. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics2 and brain-machine interfaces3. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs4,5 that can both directly interface with living tissue and adapt based on biofeedback6,7. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.