#neuromorphic computing

High Efficiency Climate Control: The Rise of Neuromorphic Bio-Adaptive Building Systems

A new framework for sustainable building automation leverages neuromorphic computing and human biofeedback to optimize indoor climates, offering a path toward household independence from massive energy bills and cloud-dependent sensors.

The HL-21-SCC multi-phase gaseous-liquid electronic circuit architecture is described here. The system integrates gaseous ionization, liquid-crystal dynamics, and piezo-semiconductive gating into a neuromorphic analog computer stabilized by the Successive Controlled Collapse (SCC) protocols, stochastic resonance via 12–18 GHz “shaker” noise tuned to the 55% chaos optimal spot, and MHD feedback maintained through a Hartmann number (Ha) greater than 0.05.

This positions the hardware as particularly suited for chaotic, non-equilibrium environments where conventional silicon architectures encounter limitations, with explicit linkages to sub-threshold neuromorphic behaviors and bio-analogous spiking patterns. A phased commercialization timeline is outlined:

Phase 1: targets 100 million to 1 billion neuron-like units for foundational adaptive spiking and chaos resilience.

Phase 2: scales to 4–5 billion units to enable hierarchical processing and fault-tolerant memory via MHD loops; and

Phase 3: reaches full 8 billion units to support emergent cognition-like traits including context-aware adaptation and self-correction.

The architecture is described as complementary to existing AI engines, providing real-time robustness to LLMs, CNNs, and transformers while preserving energy efficiency, parameter stability, and protection against “oxidative overdrive” or “surprise basin” trapping.

This framework may have a “Thought Broadcast” designation and be presented as aspirational branding for the resulting distributed global computing awareness capability.

Video- Mult-Phase Integration of computing that mimics biology: The system combines an Acoustic-Plasma Resonator (gaseous core) with a Magnetohydrodynamic (MHD) Carrier (conductive liquid) through a semiconductive solid interface. The success of the protocol is visually signaled by a Bioluminescent Surge (Ultraweak Photon Emission). High luminosity indicates successful phase-locking and synchronization, while "Photo Dimness" indicates the system has stalled or become desynchronized.

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Beyond silicon: These shape-shifting molecules could be the future of AI hardware

For more than 50 years, scientists have searched for alternatives to silicon as the foundation of electronic devices built from molecules. While the concept was appealing, practical progress proved far more difficult. Inside real devices, molecules do not behave like simple, isolated components. Instead, they interact intensely with one another as electrons move, ions shift, interfaces change, and even tiny differences in structure can trigger highly nonlinear responses. Although the potential of molecular electronics was clear, reliably predicting and controlling their behavior remained out of reach. At the same time, neuromorphic computing, hardware inspired by the brain, has pursued a similar goal. The aim is to find a material that can store information, perform computation, and adapt within the same physical structure and do so in real time. However, today's leading neuromorphic systems, often based on oxide materials and filamentary switching, still function like carefully engineered machines that imitate learning rather than materials that naturally contain it.

Iontronic Circuits: Building Intelligence in Brine

Imagine a future in which computers process information not with streams of electrons but with hydrated ions flowing through salt water, a system that mimics how the brain itself computes. This emerging field—known as iontronics, a portmanteau of ions and electronics—is rapidly growing as researchers design neuromorphic computing devices, inspired by animal nervous systems and powered by electrolyte solutions at the nanoscale [1–3]. Since Leon Chua introduced the memory resistor or “memristor” in the 1970s [4], these components have been considered revolutionary building blocks for neuromorphic computing. A memristor’s electrical resistance depends on the current that flowed through it before it was powered off, offering a way to store information. Unlike solid-state memristors, fluidic ones still face challenges in terms of scalability and integration with a circuit. A study by Patricio Ramirez of the University of Valencia in Spain and colleagues recently presented a preliminary step toward a scalable version [5]. The researchers demonstrated that nanofluidic memristors, based on membranes containing conical nanopores, can be organized into functional circuits. By arranging membranes in series and in parallel configurations, they showed how synaptic weight—that is, conductance—can be manipulated to represent brain-like properties that could be used in future logic and fluidic neuromorphic systems [6].

Neuromorphic computing, inspired by the structure of the brain, offers advantages in parallel processing, memory storage, and energy efficiency. However, current semiconductor-based neuromorphic chips require rare-earth materials and costly fabrication processes, whereas neural organoids need complex bioreactor maintenance. In this study, we explored shiitake (Lentinula edodes) fungi as a robust, sustainable alternative, exploiting its adaptive electrical signaling, which is akin to neuronal spiking. We demonstrate fungal computing via mycelial networks interfaced with electrodes, showing that fungal memristors can be grown, trained, and preserved through dehydration, retaining functionality at frequencies up to 5.85 kHz, with an accuracy of 90 ± 1%. Notably, shiitake has exhibited radiation resistance, suggesting its viability for aerospace applications. Our findings show that fungal computers can provide scalable, eco-friendly platforms for neuromorphic tasks, bridging bioelectronics and unconventional computing.