Project summary

The silicon AI rush is digging its own grave. We are building multi-billion-dollar compute complexes, burning through small nuclear power plants, and scaling parameters to infinity just to make a chatbot hallucinate slightly less. It is a thermodynamic dead end.

LivingLoop is the exit strategy. We hijack actual, living cortical networks and force them to act as a self-organizing, ultra-efficient computational layer. By wrapping a 60-electrode Microelectrode Array (MEA) with custom Digital Signal Processing (DSP) feedback , we create a hard microsecond-level digital-to-biological control loop.

This isn't philosophical speculation about machine consciousness. This is cold-blooded bio-engineering: converting digital data into electrical patterns , streaming them into a living substrate , and using real-time algorithmic feedback to forge a trainable, state-dependent bioprocessor that learns through sheer network plasticity.

What are this project's goals? How will you achieve them?

We aren't here to do "academic observation". We are here to build a functional, closed-loop biological engine that rewires itself to process information.

We break the silicon monopoly by executing a radical 5-stage pipeline:

• The Living Matrix: We cultivate raw cortical neurons on high-density glass MEA chips inside a medical-grade sterile lab , establishing a stable biological hardware baseline.

• The Micro-Intercept: We read extracellular spike trains and burst dynamics directly from the cells with absolute real-time precision.

• The Judgment Engine: Raw biological signals are instantly dumped into a high-throughput processing pipeline to classify exactly how the network is reacting to our inputs.

• Weaponizing Plasticity (Closing the Loop): We deploy a dedicated DSP hardware controller (the IFB-C layer) to hit the neurons with microsecond-accurate corrective electrical stimulation. If feedback lags, biological learning degrades into white noise. We close the loop fast enough to force structural adaptation.

• Unassailable Math: We generate a brutal, fully logged dataset tracking quantitative response shifts across multiple separate cultures. If the network adapts predictably and repeatably, the silicon scaling trap is officially bypassed.
  

How will this funding be used?

Every single dollar translates directly into laboratory hardware and biological infrastructure. The project is strictly optimized for capital efficiency.

Hard Equipment Stack (The Core Infrastructure):

• AcadeMEA System (€31k): High-fidelity 60-electrode extracellular recording and activity analysis.

• IFB-C Hardware Layer (€28k): High-speed TTL I/O, micro-stimulation, and ultra-low latency DSP feedback loops.

• SCU In Vitro Base (€16k): Core multi-headstage connectivity and routing matrix.

• Incubation & Visualization (€5k): Sanyo MCO-19AIC CO2 incubator and Olympus CKX41 inverted microscope for maintaining pristine culture lines.

Biological Materials & Operations:

• Consumables & Chips (€15k): 60MEA200/30iR-Ti-gr glass arrays, laminin/poly-D-lysine matrix coatings, and biological media batches.

• Facility Lab Rental (€12k): Secure 12-month lease within a controlled medical facility for guaranteed sterile workflows.

• Founder Execution Runway (€12k): Bare-minimum 12-month founder salary to sustain full-time R&D.

(Note: The founder is personally contributing a high-end local computing workstation equipped with an NVIDIA RTX 4090 and high-speed data logging arrays as an in-kind asset worth €5k ).

Who is on your team? What's your track record on similar projects?

Roman Stirskyi (Founder & Lead Architect): Ukrainian AI engineer and independent Spiking Neural Network (SNN) researcher.

The Edge: I am a rare breed: an engineer with a background in rocket science and control systems, who spent over 15 years in financial and investment analysis. I don't write papers for academic clout —I build systems that work under real-world economic constraints.

The Execution: I write the code, design the data logging engines, and map out the adaptive stimulation algorithms myself. I know exactly how much hardware costs , how to wire it, and how to operate heavy-duty physical R&D inside Ukraine while leveraging a clean Estonian corporate vehicle for international capital safety.

No Academic Dead Weight: I don't maintain a massive, sluggish university department. To handle the raw cell-culture mechanics, a fraction of the budget is reserved to directly rent surgical-grade medical facility space and contract battle-tested wet-lab consultants who keep the biology alive while I run the code.

What are the most likely causes and outcomes if this project fails?

1. Scientific & Plasticity WallThe Risk:

• The cultured neuronal networks might show wild, chaotic baseline variance but fail to demonstrate a statistically significant, repeatable adaptation pattern under structured stimulation protocols.

• Mitigation: The architecture is built on progressive evidence gates. Before attempting complex "learning," we benchmark intermediate steps: fixed input-to-response mapping and raw microsecond-level feedback loops. Even partial datasets mapping living network responses to high-speed digital modulation hold immediate commercial and academic value for neuromorphic and neurotech research groups.

2. Biological Instability

• The Risk: In vitro neural tissues are notoriously volatile, fragile, and prone to sudden contamination or decay.

• Mitigation: We mitigate this by establishing a secure, formal laboratory lease inside an active medical facility, strictly enforcing documented sterile protocols, and budgeting for deep redundant batches of chips and cells.

3. Operational & Regional Logistics

• The Risk: Conducting deeptech physical R&D in Ukraine presents obvious logistical and supply-chain pressures.

• Mitigation: We insulate international capital by routing all fund management, legal agreements, and IP generation through an established Estonian corporate vehicle. Physical operations are fortified via documented facility rentals, strict adherence to international Incoterms, and fully insured equipment procurement paths.
  

How much money have you raised in the last 12 months, and from where?

$0. The project has been entirely self-funded and founder-driven up to this point. There are no outstanding dilutive cap tables, toxic debt layers, or prior institutional claims on the platform's core architecture.