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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.

In this paper, the resistive switching and neuromorphic behaviour of memristive devices based on parylene, a polymer both low-cost and safe for the human body, is comprehensively studied. The Metal/Parylene/ITO sandwich structures were prepared by means of the standard gas phase surface polymerization method with different top active metal electrodes (Ag, Al, Cu or Ti of ~500 nm thickness). These organic memristive devices exhibit excellent performance: low switching voltage (down to 1 V), large OFF/ON resistance ratio (up to 10 4 ), retention (≥10 4  s) and high multilevel resistance switching (at least 16 stable resistive states in the case of Cu electrodes). We have experimentally shown that parylene-based memristive elements can be trained by a biologically inspired spike-timing-dependent plasticity (STDP) mechanism. The obtained results have been used to implement a simple neuromorphic network model of classical conditioning. The described advantages allow considering parylene-based organic memristors as prospective devices for hardware realization of spiking artificial neuron networks capable of supervised and unsupervised learning and suitable for biomedical applications.

In several biomedical applications, the detection of biomarkers demands high sensitivity, selectivity and easy-to-use devices. Organic electrochemical transistors (OECTs) represent a promising class of devices combining a minimal invasiveness and good signal transduction. However, OECTs lack of intrinsic selectivity that should be implemented by specific approaches to make them well suitable for biomedical applications. Here, we report on a biosensor in which selectivity and a high sensitivity are achieved by interfacing, in an OECT architecture, a novel gate electrode based on aptamers, Au nanoparticles and graphene hierarchically organized to optimize the final response. The fabricated biosensor performs state of the art limit of detection monitoring biomolecules, such as thrombin-with a limit of detection in the picomolar range (≤ 5 pM) and a very good selectivity even in presence of supraphysiological concentrations of Bovine Serum Albumin (BSA-1mM). These accomplishments are the final result of the gate hierarchic structure that reduces sterich indrance that could contrast the recognition events and minimizes false positive, because of the low affinity of graphene towards the physiological environment. Since our approach can be easily applied to a large variety of different biomarkers, we envisage a relevant potential for a large series of different biomedical applications.

In recent years, studies concerning Organic Bioelectronics have had a constant growth due to the interest in disciplines such as medicine, biology and food safety in connecting the digital world with the biological one. Specific interests can be found in organic neuromorphic devices and organic transistor sensors, which are rapidly growing due to their low cost, high sensitivity and biocompatibility. This trend is evident in the literature produced in Italy, which is full of breakthrough papers concerning organic transistors-based sensors and organic neuromorphic devices. Therefore, this review focuses on analyzing the Italian production in this field, its trend and possible future evolutions.

Existing methods of neurorehabilitation include invasive or non-invasive stimulators that are usually simple digital generators with manually set parameters like pulse width, period, burst duration, and frequency of stimulation series. An obvious lack of adaptation capability of stimulators, as well as poor biocompatibility and high power consumption of prosthetic devices, highlights the need for medical usage of neuromorphic systems including memristive devices. The latter are electrical devices providing a wide range of complex synaptic functionality within a single element. In this study, we propose the memristive schematic capable of self-learning according to bio-plausible spike-timing-dependant plasticity to organize the electrical activity of the walking pattern generated by the central pattern generator.

Reservoir computing systems are promising for application in bio-inspired neuromorphic networks as they allow the considerable reduction of training energy and time costs as well as an overall system complexity. Conductive three-dimensional structures with the ability of reversible resistive switching are intensively developed to be applied in such systems. Nonwoven conductive materials, due to their stochasticity, flexibility and possibility of large-scale production, seem promising for this task. In this work, fabrication of a conductive 3D material by polyaniline synthesis on a polyamide-6 nonwoven matrix was shown. An organic stochastic device with a prospective to be used in reservoir computing systems with multiple inputs was created based on this material. The device demonstrates different responses (output current) when different combinations of voltage pulses are applied to the inputs. The approach is tested in handwritten digit image classification task in simulation with the overall accuracy exceeding 96%. This approach is beneficial for processing multiple data flows within a single reservoir device.

Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle‐to‐cycle, c2c, or device‐to‐device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain‐inspired computing systems built with partially unreliable analog elements. Reservoir computing systems based on memristors are considered promising for efficient processing of temporal and dynamic data. A fully organic system with volatile polyaniline memristive devices for reservoir and nonvolatile parylene ones for spiking readout layer is presented. This system provides a simpler training procedure and greater robustness to device variability in comparison with conventional formal neural networks.

We explore and demonstrate the extension of the synapse-mimicking properties of memristive devices to a dysfunctional synapse as it occurs in the Alzheimer’s disease (AD) pathology. The ability of memristive devices to reproduce synapse properties such as LTP, LTD, and STDP has been already widely demonstrated, and moreover, they were used for developing artificial neuron networks (perceptrons) able to simulate the information transmission in a cell network. However, a major progress would be to extend the common sense of neuromorphic device even to the case of dysfunction of natural synapses. Can memristors efficiently simulate them? We provide here evidences of the ability of emulating the dysfunctional synaptic behavior typical of the AD pathology with organic memristive devices considering the effect of the disease not only on a single synapse but also in the case of a neural network, composed by numerous synapses.

The New Frontiers of Organic and Composite Nanotechnology is an attempt to illustrate current status of modern nanotechnology.The book is divided into 3 main sections, introduction and conclusion.The introduction describes general questions of the problem and main lines of the research activities.

Slime mold Physarum polycephalum is a single cell visible by an unaided eye. The slime mold optimizes its network of protoplasmic tubes to minimize expose to repellents and maximize expose to attractants and to make efficient transportation of nutrients. These properties of P. polycephalum , together with simplicity of its handling and culturing, make it a priceless substrate for designing novel sensing, computing and actuating architectures in living amorphous biological substrate. We demonstrate that, by loading Physarum with magnetic particles and positioning it in a magnetic field, we can, in principle, impose analog control procedures to precisely route active growing zones of slime mold and shape topology of its protoplasmic networks.