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.

Mycelium, the vegetative structure of fungi, exhibits complex electrical signaling patterns when stimulated that resemble neural-like activity, which can be leveraged for bio-inspired computing and sensing applications. To replicate this activity, a 100x100 grid-based circuit has been designed capable of spiking behavior and adaptable configuration thanks to memristive technology aligned with fabricated devices, mimicking the dynamics seen in mycelial networks. Simulations have been carried out to demonstrate that the memristive grid successfully replicates key aspects of mycelium’s electrical activity recorded from experimental setups, including response to environmental stimuli, spiking signal propagation, and eradication.

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.

In the era of high-performance computing, the integration of Cellular Automata (CA) principles into low-power hardware is a challenging but intriguing endeavor. At the same time, memristors have gained attention due to their potential in in-memory neuromorphic computing. As such, the concept of Wave Cellular Automata (WCA) is presented a novel computing paradigm that leverages CA principles and memristive devices for in-memory computing. However, memristive devices are subject to variability effects, which can impact their performance and, in the case of WCAs, the generation of oscillations crucial for computation. This paper explores the variability tolerance analysis of WCA both in CBRAM device level, but also in its oscillatory behavior. The analysis reveals that WCA operation remains robust even in the presence of variability, with the impact on oscillation amplitude being minor. All in all, proper circuit design and element selection play a significant role in mitigating the effects of variability.

A swarm of miniature robots comprises mini-robots collaborating to achieve common goals, inspired by the collective behavior of insects. This concept mimics decentralized cooperation, enabling complex task accomplishment across various fields such as healthcare, exploration, and rescue missions. Mini-robots operate in confined spaces and time frames with minimal energy, requiring efficient path planning to prevent collisions within their group and surroundings. Integrating ultra-low-power electronics in these robots is essential. Memristors, renowned for their low power consumption, simplicity, and high integration density, hold significant promise. This paper proposes an integrated collision avoidance memristive circuit with neuromorphic behavior for miniature robots. This circuit not only enhances swarm efficiency but also lays the groundwork for fully analog mini-robots.