This paper presents the applicability of a mycelium-inspired reservoir network through an innovative extension of the Memristive Oscillating Cellular Automata (MOCA) circuit-based network. Drawing on the adaptive, self-organizing properties of mycelium, this MOCA grid employs SiNx-based RRAMs to form reconfigurable, dynamic connections that replicate mycelial network behaviors. Using the Stanford-PKU RRAM model, the non-linear properties of the fabricated devices have been characterized, establishing a flexible reservoir network capable of transforming and encoding input signals. The network has been evaluated, demonstrating small-world characteristics, including high clustering and short average path lengths, critical for effective information propagation and complex local dynamics. The resulting adaptable circuit offers a scalable foundation for future applications in bioinspired reservoir computing.

Abstract Mycelium, the intricate vegetative network of fungi, has emerged as a promising candidate within the realm of engineered living materials (ELMs). While its intriguing structural and electrical properties highlight its potential, mycelium growth is highly sensitive to environmental conditions. To bridge this gap, a robust framework was developed to both model mycelium growth and explore its computational capabilities. This framework uses a cellular automata (CA) approach, enhanced with reaction-diffusion (RD) processes, to simulate mycelium growth under diverse environmental conditions. This configuration, combined with tunable parameters, enables the identification and validation of optimal growth patterns, supported by an algorithm designed to extract key features of hyphae–the fundamental building blocks of the mycelial network. Subsequently, the small-world properties of the modeled mycelium networks were investigated, revealing high clustering coefficients and short path lengths, characteristics that make them well-suited for reservoir computing (RC). To demonstrate their computational capabilities, mycelium-inspired RC architectures were evaluated on the MNIST dataset classification task, achieving an accuracy of up to 97.09%, highlighting the effectiveness of biologically inspired models. As a result, this framework establishes a comprehensive test-bench for mycelium modeling, growth, and computational exploration, paving the way for innovative applications in bio-inspired computing.

One of the most prominent examples of a complex system in nature is the nervous system, which exhibits oscil-lation phenomena across its structures, from single neurons to sophisticated neural networks. Memristors have been utilized in the past decade as a promising technology for building neuro-morphic systems due to their intrinsic neuromorphic properties. In this paper, we introduce a novel Memristor-based Oscillator (MBO) circuit design that implements both artificial neurons and artificial synapses in the same MBO-based medium, addressing scalability issues. The circuit design is transistor-free and simple, allowing high integration density. We utilize passive unipolar memristor devices based on the JART memristor model to design MBO neurons, which have been shown to reproduce bio-plausible spiking and bursting activities. Moreover, the MBO circuit is also realized as an artificial synapse incorporating synapse and axon mechanisms. Finally, the MBO neurons are modeled as Parkinson-related neuron types and their direct bidirectional coupling demonstrates that MBO neurons are effective in driving spiking activity in non-externally stimulated MBO neurons. This qualifies them for implementation in brain-inspired networks, providing low-cost Neurological-disease-related emulators.

The memristor crossbar architecture is a new technology that combines memory and computing on the same chip, finding numerous applications in modern bio-inspired computing systems. Recently, memristor-coupled Chua Circuit Networks (MCCNs) have been developed for the experimental confirmation of collective nonlinear phenomena, such as chimera states, that are also observed in the brain. For highly dense topologies, however, memristor crossbars can be prone to certain vulnerabilities. In this paper, we investigate the impact of sneak-path currents (SPCs) on the collective behaviors of chaotic oscillator networks, uncovering the network's tolerance to various realistic memristor crossbar designs. Despite the fact that these states alter the synchronization regime map, our findings suggest that SPCs have no detrimental impact on the formation and stability of single or multiple chimera states. This coupling issue along with other possible challenges are thoroughly discussed with a focus on nonlinear dynamics, highlighting the reliability of memristor crossbars as a coupling mechanism for studying chimera states.

Synchronization transition in neuromorphic networks has attracted much attention recently as a fundamental property of biological neural networks, which relies on network connectivity along with different synaptic features. In this work, an area-optimized FPGA implementation of an Asynchronous Cellular Automata Neuron model that exhibits discrete-state neuron dynamics is introduced. The proposed neuron model is capable of reproducing various neuromorphic oscillations observed in biological neurons using less hardware resources than previous implementations. We investigate synchronization transitions with a focus on the emergence of chimera states in a ring-based network consisting of hardware-based neurons with electrical synaptic coupling. In particular, we study the effects on the network’s phase synchronization through changing two control parameters: the coupling range and the coupling strength. We indicate that via proper configuration of the coupling parameters, we influence the synchronization transition and reveal chimera states which have been associated with neurological disorders.

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules - a technical goal undoubtedly demanding for novel devices. Following the trend, we explore an unconventional route of implementing reservoir computing on a single protein molecule and introduce neuromorphic connectivity with a small-world networking property. We have chosen Izhikevich spiking neurons as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a `hardware' architecture of the communication networks connecting the processors. We apply on a single readout layer, various training methods in a supervised fashion to investigate whether the molecular structured Reservoir Computing (RC) system is capable to deal with machine learning benchmarks. We start with the Remote Supervised Method, based on Spike-Timing-Dependent-Plasticity, and carry on with linear regression and scaled conjugate gradient back-propagation training methods. The RC network is evaluated as a proof-of-concept on the handwritten digit images from the standard MNIST and the extended MNIST datasets and demonstrates acceptable classification accuracies in comparison with other similar approaches.

The time series of the brain are usually characterized by the co-existence of synchronized and desynchronized behaviors. This kind of behavior is related to normal and disorderly functions of the brain. One of the suggested mechanisms to understand thoroughly this behavior are chimera states, which are characterized by the coincidence of coherent and incoherent dynamics that can be exploited through networks of symmetrically coupled identical oscillators. In this work, ring-based networks of Chua's circuits, the simplest electronic oscillators that perform chaotic and well-known bifurcation phenomena, have been extensively studied in memristive crossbars (Xbar), revealing various collective spatio-temporal behaviors, such as chimera states. With respect to different Xbar connectivities and via SPICE-level circuit simulations, the proposed Xbar system proves its efficacy to reproduce spatio-temporal patterns spanning from complete synchronization and chimera states up to fully chaotic states.