In recent years, the intrinsic neuromorphic properties of memristor devices have led to their extensive use in brain-inspired circuits and systems. The development of brain-inspired (nano)circuits incorporating memristors has the potential to revolutionize the field of neuromorphic engineering. This study presents a novel circuit design of Basal Ganglia (BG), with the Memristor-Based Oscillator (MBO), which takes a significant step forward in this direction. The MBO circuit utilizes low-power fitted Conductive-Bridging RAM (CBRAM) devices with distinct neuromorphic features, making it a promising tool for both research and treatment of neurodegenerative diseases such as Parkinson’s Disease (PD). Additionally, the circuit incorporates modeled synapses exhibiting both excitatory and inhibitory behavior and is calibrated to match the performance of fabricated devices. The MBO circuit is well-suited for incorporation in brain-inspired networks and can accurately replicate neuronal behavior, resulting to a low-cost emulator for PD and other neurodegenerative diseases compared with other hardware approaches. In this study, the Parkinsonian Network (PN) was replicated and then the Deep Brain Stimulation (DBS) methodology was applied, showing promising results for the development of memristor-based brain-inspired nanocircuits.

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.

Recognition of Parkinson's disease (PD) from neurophysiological signals remains a key challenge for early diagnosis and intervention. For real‐time monitoring, it requires advanced signal processing that operates directly on wearable or portable devices, thereby reducing latency, preserving privacy, and supporting continuous assessment. This study presents a bio‐inspired computational framework that combines electroencephalogram (EEG)‐derived stimulation with a mycelium‐inspired memristive reservoir circuit for analog pattern recognition. Resting‐state EEG recordings from individuals with PD and healthy controls undergo artifact removal and spectral decomposition. Dominant frequency components are transformed into discrete voltage pulse sequences that emulate cortical activity patterns and drive a fixed mycelial lattice. Electrical connectivity and branching density within the mycelium‐inspired network determine nonlinear signal propagation, producing frequency‐dependent activation dynamics. The memristive reservoir functions as a dynamic medium that implicitly encodes temporal and spectral information through state transitions in its conductive pathways. Voltage integration across selected nodes enables analog separability between Parkinsonian and control conditions without explicit digital processing or trained classifiers. The proposed system is presented as a proof‐of‐concept hybrid neuromorphic platform that bridges EEG dynamics, living‐material architectures, and memristive computation, highlighting its potential for analog bio‐inspired recognition of neurodegenerative disorders.