Organic neuromorphic platforms have recently received growing interest for the implementation and integration of hybrid systems, acting as a bridge between biological tissue and artificial computing architectures.
Abstract Synaptic plasticity is a fundamental process for neuronal communication and is involved in neurodegeneration. This process has been recently exploited to inspire the design of next‐generation bioelectronic platforms. Neuromorphic devices have emerged as ideal candidates in mimicking brain functionalities, thanks to their ionic‐to‐electronic signal transduction, biocompatibility, and their ability to display short‐ and long‐term memory as biological synapses. However, these devices still fail in bridging the gap between electronics and biological systems due to the lack of biomimetic features. Here, a biomembrane‐based organic electrochemical transistor (OECT) is implemented and the supported‐lipid‐bilayer‐mediated short‐term depression of the device is investigated. After morphological and electrical characterization of the lipid bilayer, its ionic barrier behavior is exploited to enhance the neuromorphic operation of the OECT. Such biomimetic neuromorphic devices pave the way toward the implementation of synapses‐resembling in vitro platforms to investigate and characterize neurodegenerative processes involving synaptic plasticity loss.
The optimization of the cell–chip coupling is one of the major challenges in bioelectronics. The cell–electrode interface is typically represented by an equivalent electrical circuit that can simulate the electrical behavior of neuronal cells coupled to microelectrodes. However, these circuital models do not take into account the highly dynamic mechanical behavior of cells. In fact, cells constantly remodel their cytoskeleton to preserve or adapt their shape to external mechanical cues. Hereby, we present a mathematical model along with a systems theory approach to numerical simulations, in order to study and predict cell–electrode interface dynamics over time. Both planar and pseudo-3D electrode designs have been considered, and their effect on the cell coupling for extracellular recordings has been investigated. In turn, this dynamic model can be exploited to provide fundamental parameters for future design of microelectrode arrays.
Abstract The computation of the brain relies on the highly efficient communication among billions of neurons. Such efficiency derives from the brain’s plastic and reconfigurable nature, enabling complex computations and maintenance of vital functions with a remarkably low power consumption of only ∼20 W. First efforts to leverage brain-inspired computational principles have led to the introduction of artificial neural networks that revolutionized information processing and daily life. The relentless pursuit of the definitive computing platform is now pushing researchers towards investigation of novel solutions to emulate specific brain features ( such as synaptic plasticity) to allow local and energy efficient computations. The development of such devices may also be pivotal in addressing major challenges of a continuously aging world, including the treatment of neurodegenerative diseases. To date, the neuroelectronics field has been instrumental in deepening the understanding of how neurons communicate, owing to the rapid development of silicon-based platforms for neural recordings and stimulation. However, this approach still does not allow for in loco processing of biological signals. In fact, despite the success of silicon-based devices in electronic applications, they are ill-suited for directly interfacing with biological tissue. A cornucopia of solutions has therefore been proposed in the last years to obtain neuromorphic materials to create effective biointerfaces and enable reliable bidirectional communication with neurons. Organic conductive materials in particular are not only highly biocompatible and able to electrochemically transduce biological signals, but also promise to include neuromorphic features, such as neuro-transmitter mediated plasticity and learning capabilities. Furthermore, organic electronics, relying on mixed electronic/ionic conduction mechanism, can be efficiently coupled with biological neural networks, while still successfully communicating with silicon-based electronics. Here, we envision neurohybrid systems that integrate silicon-based and organic electronics-based neuromorphic technologies to create active artificial interfaces with biological tissues. We believe that this approach may pave the way towards the development of a functional bidirectional communication between biological and artificial ‘brains’, offering new potential therapeutic applications and allowing for novel approaches in prosthetics.
