In this phase, we will pinpoint the most promising hardware and software approaches to augment our living neural tissue computers and accelerate progress toward AGI. We will evaluate non-traditional paradigms including temporal logic circuits, race logic designs, analog and in-memory processors. To bridge brain and silicon, we are considering the inclusion of cutting-edge high-density electrode arrays for finer neural control, network‐attached storage (NAS) for seamless data flow, and SingluartityNET aligned hardware modules (Temporal logic circuits, Race logic designs, Analog processors, In-memory computing architectures, FPGAs) to translate symbolic commands into spike patterns in real time. Through our research, we will vet the optimal components that boost performance, energy efficiency, and adaptability. Mapping of SingularityNET’s Hyperon modules onto a bio-silicon pipeline is novel because it unites real neural processing with programmable electronics, creating an adaptable, energy-efficient route to AGI.

Intactis Bio Corp. will deliver a comprehensive research plan that lays out the evaluated hardware paradigms, and will detail how we intend to augment our neural tissue platform with electrode arrays, NAS solutions, and FPGAs. Importantly, it will explain that these components are candidates to be tested and that our experiments will determine which models deliver the best signal fidelity, throughput, and energy savings. By mapping SingularityNET’s Hyperon modules (PLN inference, MOSES recursion, ECAN attention) onto our bio-silicon pipeline, we’ll create repeatable tests for performance, power draw, and scalability. This deliverable not only compares new hardware to GPUs and TPUs but also shows how the best silicon systems can amplify the unique benefits of living neural networks.

$10,000 USD

This milestone will be achieved when we produce a clear, actionable plan that identifies at least four computing paradigms to integrate with our neural biochip. The plan must explain our criteria for selecting the best versions of these components, focusing on signal precision, latency, and energy per operation. We must describe how we will test them in lab experiments. Success means the plan convincingly shows how integrating the right silicon systems with neural tissue yields measurable improvements over traditional accelerators in AGI-relevant tasks like recursive reasoning and dynamic attention. Finally, the document must highlight how this blended bio-silicon strategy is a novel leap toward energy-efficient, adaptive AGI systems. - Hardware Paradigms Catalog: List and rationale for four computing approaches. - Bio-Silicon Integration Map: Diagram showing novel computing integration data flow. - Hyperon Module Mapping: Outline of PLN, MOSES, and ECAN integration steps. - Test Protocols Overview: Initial benchmarking and experiment plan. - Novelty Highlighted: Bio-silicon co-design clearly justified.

We will carry out a concise, practical review of the most promising non-traditional computing approaches for AGI, focusing especially on our novel neural tissue computers alongside temporal logic circuits, analog memory arrays, race-logic designs, and modern neuromorphic chips. This review will identify how each system handles key AGI challenges like step-wise reasoning, uncertainty-based inference, and dynamic attention. By comparing energy use, speed, and flexibility, we will show where living neural tissue offers real advantages over GPUs, TPUs, and purely silicon-based neuromorphic hardware. Finally, we will translate these insights into a clear set of side-by-side tests that measure raw performance, scaling potential, power draw, and the accuracy of converting symbolic instructions into biological signals. What sets our work apart is that we are not merely surveying hardware in isolation: we will directly benchmark each silicon paradigm against real neural tissue using the same tasks and metrics, creating the first side-by-side evaluation of symbolic-to-spike translation accuracy, scalability, power draw, and overall performance. By translating these insights into a clear test suite, we will establish a repeatable framework for quantitatively comparing biological and silicon approaches, revealing exactly where our biocomputer outperforms or complements traditional GPUs, TPUs, and neuromorphic designs.

Our literature review deliverable is a written report summarizing recent studies on each computing paradigm, with special emphasis on how living neural tissue computers translate symbolic queries into spike patterns and back again. This literature review will include a benchmarking guide that defines four core measures: task throughput, scalability, energy per operation, and signal-conversion accuracy. A comparison table will highlight how neural tissue computers stack up against GPUs, TPUs, analog in-memory devices, and digital neuromorphic chips.

$10,000 USD

This milestone is successful if the literature review covers at least fifty key publications since 2020 and clearly explains in everyday language how each technology handles reasoning, inference, and attention. The benchmarking guide must include four well-defined, reproducible tests with step-by-step instructions on how to conduct benchmarking equations. The comparison table will highlight at least three areas where neural tissue computers outperform conventional accelerators in energy efficiency or biological fidelity, and it should also point out any trade-offs. - 50+ Publications Reviewed: Comprehensive coverage since 2020. - Literature Synthesis Report: Plain-language summaries of key studies. - Benchmark Criteria Guide: Definitions and measurement protocols for four metrics, throughput, scalability, energy, fidelity. - Comparison Matrix: Table contrasting neural tissue with other accelerators. - Statistical Plan: Outline of paired t-tests or ANOVA (α=0.05) for future results.

In this phase, we will purchase our updated hardware identified in Milestones 1 & 2, code any necessary software integrations, grow iPSC derived neural cells in the laboratory, and test system performance in the laboratory. This milestone will first test basic integrations and signal processing in the platform. From there we will explore key AGI challenges such as multi-step reasoning, uncertainty-driven inference, and dynamic attention by encoding tasks into spike-timing patterns, letting the tissue and silicon co-process them, and decoding the results. This work is novel because it goes beyond proof-of-concept: we will be the first to measure how each specific hardware paradigm shapes live biocomputation and influences AGI architectures under real-world lab conditions.

We will deliver a concise presentation of our early results, highlighting both simulated runs and preliminary lab data from our integrated bio-silicon platform. This report will show how tasks are encoded into our FPGA-driven pulse patterns, how the neural tissue responds via the electrode array, and how we route outputs through analog or in-memory modules for further inference. Key performance metrics include speed of execution, energy per operation, and fidelity of symbol-to-spike conversion. These metrics will be analyzed against initial benchmarks. Accompanying these findings, we will update our system documentation to reflect real-world behavior, noting any unexpected interactions and refining our hardware-software interfaces based on learnings.

$50,000 USD

This milestone will be deemed successful if we demonstrate a working hybrid experiment, either in simulation or on the benchtop, where a defined AGI-style task is sent into our neural biochip, co-processed with a chosen silicon accelerator, and then decoded back into symbolic form. We must show clear data on at least two AGI-relevant operations (for example, a three-step recursive query and a probabilistic inference task), reporting execution times, energy use, and conversion accuracy within 15% of our targets. - Hardware Procurement: Hardware on-site and set-up at Intactis Lab. - Reliable AGI Prototype: ≥5 successful simulated runs on AGI-style task, ≥90% task accuracy. - In-vitro test: 1 in-vitro run to test AGI style task integration with living neuron biochip. Cost & Integration Clear: Budget and setup complexity documented.

In this final phase, we will bring together all of our insights, hardware tests, and software frameworks to deliver a comprehensive evaluation of the most promising biocomputing architectures. Building on our earlier work, we will compare how each silicon‐enhanced neural tissue setup fared in real AGI‐style tasks, looking at speed, energy use, reliability, and ease of integration. We will assess not only technical performance but also practical factors like component cost, lab setup complexity, and long‐term scalability. This milestone culminates in a working prototype or proof-of-concept system, where one standout hardware paradigm will be showcased running a clear AGI use case, such as a multi-step reasoning problem with adaptive attention. This live demonstration will underline the novelty of our bio-silicon co-design, proving that living networks and programmable electronics can together handle real-world, symbol-based intelligence tasks.

We will hand over two key items. First, a final report that lays out side-by-side comparisons of every tested architecture, complete with charts and narrative summaries covering feasibility, energy and speed metrics, component and maintenance costs, and how smoothly each system integrates into our existing lab pipeline. This report will highlight trade-offs and recommend the best path forward for scaling up. Second, we will deliver a working prototype or demo setup: a self-contained biocomputer that uses our chosen hardware paradigm integrated with 3D-printed neural tissue and supporting silicon modules. In this proof-of-concept, the system will execute an AGI-relevant task, from encoding a recursive query into spike patterns, to applying probabilistic inference, to dynamically shifting attention based on results, all in real time. A short video walkthrough and accompanying user guide will document the demonstration, ensuring others can reproduce and build upon our success.

$10,000 USD

We will consider this milestone achieved when we submit a final report that clearly shows which architecture delivers the best balance of performance, energy efficiency, cost, and integration ease. The report must compare at least three complete bio-silicon configurations, quantify their strengths and weaknesses, and offer a justified recommendation. The prototype demonstration must run an end-to-end AGI-style task, with measured results that meet or exceed our targets for execution time and energy per operation. The prototype should operate consistently over multiple trials, proving reliability, and should be documented in a reproducible guide. If we can show that our novel integration of living neural tissue and a selected silicon paradigm not only works but offers clear advantages over traditional accelerators, then Milestone 4 will be successfully completed. -Demonstrate An AGI Relevant Task: Show end-to-end results for an AGI-style tasks. -Meet Performance Targets: Execution time and energy use within 15% of our goals. -Prove Statistical Gains: Improvements over a digital-only control with t-test, p < 0.05. -Update All Protocols: Complete lab procedures and interface docs revised. -On Time & Budget: Total project within 6 months and under $80 K USD.