Coupling Resistive Switching Devices with Neurons: State of the Art and Perspectives

Interfacing Biology and Electronics with Memristive Materials

Memristive technologies promise to have a large impact on modern electronics, particularly in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile, the evolution of memristive materials alongside the technological progress is opening application perspectives also in the biomedical field, particularly for implantable and lab-on-a-chip devices where advanced sensing technologies generate a large amount of data. Memristive devices are emerging as bioelectronic links merging biosensing with computation, acting as physical processors of analog signals or in the framework of advanced digital computing architectures. Recent developments in the processing of electrical neural signals, as well as on transduction and processing of chemical biomarkers of neural and endocrine functions, are reviewed. It is concluded with a critical perspective on the future applicability of memristive devices as pivotal building blocks in bio-AI fusion concepts and bionic schemes.

Automatized offline and online exploration to achieve a target dynamics in biohybrid neural circuits built with living and model neurons

Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build effective artificial intelligence and brain hybridization. In this work, we deal with the automatized online and offline adaptation, exploration and parameter mapping to achieve a target dynamics in hybrid circuits and, in particular, those that yield dynamical invariants between living and model neurons. We address dynamical invariants that form robust cycle-by-cycle relationships between the intervals that build neural sequences from such interaction. Our methodology first attains automated adaptation of model neurons to work in the same amplitude regime and time scale of living neurons. Then, we address the automatized exploration and mapping of the synapse parameter space that lead to a specific dynamical invariant target. Our approach uses multiple configurations and parallel computing from electrophysiological recordings of living neurons to build full mappings, and genetic algorithms to achieve an instance of the target dynamics for the hybrid circuit in a short time. We illustrate and validate such strategy in the context of the study of functional sequences in neural rhythms, which can be easily generalized for any variety of hybrid circuit configuration. This approach facilitates both the building of hybrid circuits and the accomplishment of their scientific goal.

Memristor-Based Neuromodulation Device for Real-Time Monitoring and Adaptive Control of Neuronal Populations

Neurons are specialized cells for information transmission and information processing. In fact, many neurologic disorders are directly linked not to cellular viability/homeostasis issues but rather to specific anomalies in electrical activity dynamics. Consequently, therapeutic strategies based on the direct modulation of neuronal electrical activity have been producing remarkable results, with successful examples ranging from cochlear implants to deep brain stimulation. Developments in these implantable devices are hindered, however, by important challenges such as power requirements, size factor, signal transduction, and adaptability/computational capabilities. Memristors, neuromorphic nanoscale electronic components able to emulate natural synapses, provide unique properties to address these constraints, and their use in neuroprosthetic devices is being actively explored. Here, we demonstrate, for the first time, the use of memristive devices in a clinically relevant setting where communication between two neuronal populations is conditioned to specific activity patterns in the source population. In our approach, the memristor device performs a pattern detection computation and acts as an artificial synapse capable of reversible short-term plasticity. Using in vitro hippocampal neuronal cultures, we show real-time adaptive control with a high degree of reproducibility using our monitor-compute-actuate paradigm. We envision very similar systems being used for the automatic detection and suppression of seizures in epileptic patients.

Fungal electronics

Fungal electronics is a family of living electronic devices made of mycelium bound composites or pure mycelium. Fungal electronic devices are capable of changing their impedance and generating spikes of electrical potential in response to external control parameters. Fungal electronics can be embedded into fungal materials and wearables or used as stand alone sensing and computing devices.

Topological Control of Magnetic Textures

A micromagnetic study is carried out on the role of using topology to stabilize different magnetic textures, such as a vortex or an anti-vortex state, in a magnetic heterostructure consisting of a Permalloy disk coupled to a set of nanomagnetic bars. The topological boundary condition is set by the stray field contributions of the nanomagnet bars and thus by their magnetization configuration, and can be described by a discretized winding number that will be matched by the winding number of the topological state set in the disk. The lowest number of nanomagnets that defines a suitable boundary is four, and we identify a critical internanomagnet angle of 225° between two nanomagnets, at which the boundary fails because the winding number of the nanomagnet configuration no longer controls that of the disk magnetization. The boundary also fails when the disk-nanomagnets separation is > 50 nm and for disk diameters > 480 nm. Finally, we provide preliminary experimental evidence from magnetic force microscopy studies in which we demonstrate that an energetically unstable, anti-vortex-like structure can indeed be stabilized in a Permalloy disk, provided that the appropriate topological conditions are set.

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.

Memristive TiO2: Synthesis, Technologies, and Applications

Titanium dioxide (TiO2) is one of the most widely used materials in resistive switching applications, including random-access memory, neuromorphic computing, biohybrid interfaces, and sensors. Most of these applications are still at an early stage of development and have technological challenges and a lack of fundamental comprehension. Furthermore, the functional memristive properties of TiO2 thin films are heavily dependent on their processing methods, including the synthesis, fabrication, and post-fabrication treatment. Here, we outline and summarize the key milestone achievements, recent advances, and challenges related to the synthesis, technology, and applications of memristive TiO2. Following a brief introduction, we provide an overview of the major areas of application of TiO2-based memristive devices and discuss their synthesis, fabrication, and post-fabrication processing, as well as their functional properties.

A biohybrid synapse with neurotransmitter-mediated plasticity

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 ANNs^4,5 that can both directly interface with living tissue and adapt based on biofeedback^6,7. 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.

A biohybrid synapse with neurotransmitter-mediated plasticity

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 ANNs^4,5 that can both directly interface with living tissue and adapt based on biofeedback^6,7. 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.

Memristive Behavior Enabled by Amorphous-Crystalline 2D Oxide Heterostructure

The emergence of memristive behavior in amorphous-crystalline 2D oxide heterostructures, which are synthesized by atomic layer deposition (ALD) of a few-nanometer amorphous Al₂ O₃ layers onto atomically thin single-crystalline ZnO nanosheets, is demonstrated. The conduction mechanism is identified based on classic oxygen vacancy conductive channels. ZnO nanosheets provide a 2D host for oxygen vacancies, while the amorphous Al₂ O₃ facilitates the generation and stabilization of the oxygen vacancies. The conduction mechanism in the high-resistance state follows Poole-Frenkel emission, and in the the low-resistance state is fitted by the Mott-Gurney law. From the slope of the fitting curve, the mobility in the low-resistance state is estimated to be ≈2400 cm² V^-1 s^-1 , which is the highest value reported in semiconductor oxides. When annealed at high temperature to eliminate oxygen vacancies, Al is doped into the ZnO nanosheet, and the memristive behavior disappears, further confirming the oxygen vacancies as being responsible for the memristive behavior. The 2D heterointerface offers opportunities for new design of high-performance memristor devices.

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale cross-bar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a new-generation robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.

Bioinspired bio-voltage memristors

Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated.

Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular, we have shown that sensory neurons in the input layer of SNNs can simultaneously encode the input signal based both on the spiking frequency rate and on varying the latency in generating spikes. In the case of such mixed temporal-rate coding, the SNN should implement learning working properly for both types of coding. Based on this, we investigate how a single neuron can be trained with pure rate and temporal patterns, and then build a universal SNN that is trained using mixed coding. In particular, we study Hebbian and competitive learning in SNN in the context of temporal and rate coding problems. We show that the use of Hebbian learning through pair-based and triplet-based spike timing-dependent plasticity (STDP) rule is accomplishable for temporal coding, but not for rate coding. Synaptic competition inducing depression of poorly used synapses is required to ensure a neural selectivity in the rate coding. This kind of competition can be implemented by the so-called forgetting function that is dependent on neuron activity. We show that coherent use of the triplet-based STDP and synaptic competition with the forgetting function is sufficient for the rate coding. Next, we propose a SNN capable of classifying electromyographical (EMG) patterns using an unsupervised learning procedure. The neuron competition achieved via lateral inhibition ensures the “winner takes all” principle among classifier neurons. The SNN also provides gradual output response dependent on muscular contraction strength. Furthermore, we modify the SNN to implement a supervised learning method based on stimulation of the target classifier neuron synchronously with the network input. In a problem of discrimination of three EMG patterns, the SNN with supervised learning shows median accuracy 99.5% that is close to the result demonstrated by multi-layer perceptron learned by back propagation of an error algorithm.

Memristive plasticity in artificial electrical synapses via geometrically reconfigurable, gramicidin-doped biomembranes

It is now known that mammalian brains leverage plasticity of both chemical and electrical synapses (ES) for collocating memory and processing. Unlike chemical synapses, ES join neurons via gap junction ion channels that permit fast, threshold-independent, and bidirectional ion transport. Like chemical synapses, ES exhibit activity-dependent plasticity, which modulates the ionic conductance between neurons and, thereby, enables adaptive synchronization of action potentials. Many types of adaptive computing devices that display discrete, threshold-dependent changes in conductance have been developed, yet far less effort has been devoted to emulating the continuously variable conductance and activity-dependent plasticity of ES. Here, we describe an artificial electrical synapse (AES) that exhibits voltage-dependent, analog changes in ionic conductance at biologically relevant voltages. AES plasticity is achieved at the nanoscale by linking dynamical geometrical changes of a host lipid bilayer to ion transport via gramicidin transmembrane ion channels. As a result, the AES uniquely mimics the composition, biophysical properties, bidirectional and threshold-independent ion transport, and plasticity of ES. Through experiments and modeling, we classify our AES as a volatile memristor, where the voltage-controlled conductance is governed by reversible changes in membrane geometry and gramicidin channel density. Simulations show that AES plasticity can adaptively synchronize Hodgkin-Huxley neurons. Finally, by modulating the molecular constituents of the AES, we show that the amplitude, direction, and speed of conductance changes can be tuned. This work motivates the development and integration of ES-inspired computing devices for achieving more capable neuromorphic hardware.

A Neuromorphic Device Implemented on a Salmon-DNA Electrolyte and its Application to Artificial Neural Networks

A bioinspired neuromorphic device operating as synapse and neuron simultaneously, which is fabricated on an electrolyte based on Cu2+-doped salmon deoxyribonucleic acid (S-DNA) is reported. Owing to the slow Cu2+ diffusion through the base pairing sites in the S-DNA electrolyte, the synaptic operation of the S-DNA device features special long-term plasticity with negative and positive nonlinearity values for potentiation and depression (αp and αd), respectively, which consequently improves the learning/recognition efficiency of S-DNA-based neural networks. Furthermore, the representative neuronal operation, "integrate-and-fire," is successfully emulated in this device by adjusting the duration time of the input voltage stimulus. In particular, by applying a Cu2+ doping technique to the S-DNA neuromorphic device, the characteristics for synaptic weight updating are enhanced (|αp|: 31→20, |αd|: 11→18, weight update margin: 33→287 nS) and also the threshold conditions for neuronal firing (amplitude and number of stimulus pulses) are modulated. The improved synaptic characteristics consequently increase the Modified National Institute of Standards and Technology (MNIST) pattern recognition rate from 38% to 44% (single-layer perceptron model) and from 89.42% to 91.61% (multilayer perceptron model). This neuromorphic device technology based on S-DNA is expected to contribute to the successful implementation of a future neuromorphic system that simultaneously satisfies high integration density and remarkable recognition accuracy.

A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks

Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.

Ultrathin Conformable Organic Artificial Synapse for Wearable Intelligent Device Applications

Ultrathin conformable artificial synapse platforms that can be used as on-body or wearable chips suggest a path to build next-generation, wearable, intelligent electronic systems that can mimic the synaptic operations of the human brain. So far, an artificial synapse architecture with ultimate mechanical flexibility in a freestanding form while maintaining its functionalities with high stability and accuracy on any conformable substrate has not been demonstrated yet. Here, we demonstrate the first ultrathin artificial synapse (∼500 nm total thickness) that features freestanding ferroelectric organic neuromorphic transistors (FONTs), which can stand alone without a substrate or an encapsulation layer. Our simple dry peel-off process allows integration of the freestanding FONTs with an extremely thin film that is transferable to various conformable substrates. The FONTs exhibit excellent and reliable synaptic functions, which can be modulated by diverse electrical stimuli and relative timing (or temporal order) between the pre- and postsynaptic spikes. Furthermore, the FONTs show sustainable synaptic plasticity even under folded condition ( R = 50 μm, ε = 0.48%) for more than 6000 input spikes. Our study suggests that the ultrathin conformable organic artificial synapse platforms are considered as one of key technologies for realization of wearable intelligent electronics in the future.

Inkjet assisted fabrication of planar biocompatible memristors

In this study we address a novel design of a planar memristor and investigate its biocompatibility. An experimental prototype of the proposed memristor assembly has been manufactured using a hybrid nanofabrication method, combining sputtering of electrodes, patterning the insulating trenches, and filling them with a memristive substance. To pattern the insulating trenches, we have examined two nanofabrication techniques employing either a focused ion beam or a cantilever tip of an atomic force microscope. Inkjet printing has been used to fill the trenches with the functional titania ink. The experimental prototypes have qualitatively demonstrated memristive current–voltage behavior, as well as high biocompatibility.

A planar memristor was fabricated by a hybrid method combining AFM patterning and inkjet printing.

Progress in Neuroengineering for brain repair: New challenges and open issues

Background

In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome.

Results

In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise.

Conclusions

Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.

Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC.