Spiking neuromorphic networks (SNNs) are bio-inspired artificial systems capable of unsupervised learning and promising candidates to mimic biological neural systems in efficient solution of ...cognitive tasks. Most SNNs are based on local learning rules, such as bio-like spike-time-dependent plasticity (STDP). In this paper, we report a significantly improved timescale of STDP for polyaniline-based memristive microdevices. We have used this result to show the possibility of associative learning with an unsupervised STDP-like mechanism of a simple SNN. The dependence of the required number of learning cycles on the pulse length was found: the longer the training pulse, the smaller the number of epochs the system needs to learn the associative rule. But the total training time remained nearly constant regardless of the pulse length. This study will be helpful in designing more sophisticated bio-plausible neuromorphic systems based on organic memristors.
One of the remarkable features of the emerging neuromorphic systems is the ability of implementing in‐memory computing which is demonstrated using memristors to realize both memory and computation ...functionalities within a single element. However, biological neural systems exhibit many other outstanding computing capabilities, among which one is the sensitivity to temporal parameters of neural activity. The identification and the realization of systems able to imitate this ability is still a very challenging perspective. Herein, polyaniline‐based organic memristive devices endowed with volatile resistive switching, complex temporal behaviors and capable of processing 4‐bit sequences of data with reliable separation of states are demonstrated. Thanks to this ability, such devices can be key elements in a reservoir layer of a network to map high‐dimensional input signals to a lower‐dimensional feature space. Herein, it is demonstrated through simulations that this type of device could be a valuable element for the realization of a reservoir computing system for the classification of handwritten digits from MNIST dataset. The model suggests that the electrical properties of the polyaniline‐based organic memristive devices ensure the realization of a system able to correctly classify handwritten digits and to be tolerant to considerable overlapping of neighboring reservoir states.
The experiment‐based software model of a reservoir computing system is used for classification of handwritten digits from MNIST dataset. Polyaniline organic memristive devices provide nonlinear mapping of input signals to a lower‐dimensional feature space by processing 4‐bit sequences of data. The system shows great robustness to dispersion of resistive states of the memristive devices.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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
), retention (≥10
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.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
In hardware neuromorphic systems (NSs), memristors are used as synaptic connections. In such systems, spike‐timing‐dependent plasticity (STDP) is a promising local learning rule. Herein, STDP is ...studied in a system composed of a pair of hardware or software neurons connected by a (CoFeB)x(LiNbO3)100−x nanocomposite‐based memristor. The dopamine‐like modulation of memristor‐based STDP is implemented simply by the change in the polarity of spikes generated by artificial neurons operating in the inhibitory or excitatory mode. This modulation method is shown to be compatible with hardware neurons, different spike shapes, and is used in spiking NSs with bioinspired dopamine‐like reinforcement learning.
Spike‐timing‐dependent plasticity (STDP) is demonstrated on hardware and software neurons connected by nanocomposite memristors. Dopamine‐like STDP modulation is implemented by change in spikes polarity. This method is compatible with hardware neurons operating in inhibitory or excitatory mode. The modulation method is shown to be suitable for various types of spikes and is used in spiking neuromorphic systems with reinforcement learning.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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.
Currently, there is growing interest in wearable and biocompatible smart computing and information processing systems that are safe for the human body. Memristive devices are promising for solving ...such problems due to a number of their attractive properties, such as low power consumption, scalability, and the multilevel nature of resistive switching (plasticity). The multilevel plasticity allows memristors to emulate synapses in hardware neuromorphic computing systems (NCSs). The aim of this work was to study Cu/poly-
-xylylene(PPX)/Au memristive elements fabricated in the crossbar geometry. In developing the technology for manufacturing such samples, we took into account their characteristics, in particular stable and multilevel resistive switching (at least 10 different states) and low operating voltage (<2 V), suitable for NCSs. Experiments on cycle to cycle (C2C) switching of a single memristor and device to device (D2D) switching of several memristors have shown high reproducibility of resistive switching (RS) voltages. Based on the obtained memristors, a formal hardware neuromorphic network was created that can be trained to classify simple patterns.
For estimation of toxicity of silver nanoparticles under long-term exposure for mammals and humans, the accumulation of silver in mice tissues (blood, liver, brain) during 2 and 4 months experiments ...was examined. Neutron activation analysis revealed silver in all examined tissues with its highest concentrations in liver followed by brain (including silver in blood vessels). The lowest concentration of silver was observed in blood samples. The mean specific mass content of silver which crossed the blood–brain barrier was 225 ± 99 ng (for male) and 395 ± 150 ng (for female) of the brain sample after 2 months of administration, 860 ± 200 ng (for male) and 880 ± 200 ng (for female) of brain sample after 4 months of administration. The obtained results are of great importance for nanotoxicological studies.
The memristive elements constructed using polymers - polyaniline (PANI) and polyethyleneoxide (PEO) - could be assembled on planar thin films or on 3D fibrous materials. Planar conductive PANI-based ...materials were made using the Langmuir-Schaefer (LS) method, and the 3D materials - using the electrospinning method which is a scalable technique. We have analyzed the influence of PANI molar mass, natures of solvent and subphase on the crystalline structure, thickness and conductivity of planar LS films, and the influence of PANI molar mass and the PANI-PEO ratio on the morphological and structural characteristics of 3D fibrous materials.
We discuss the effect of structure formation of Langmuir polyaniline layers on the performance of memristive thin-film elements as well as the morphology and conductivity of electrospinned PANI-PEO nonwovens.
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