qubic

While the crypto world is often distracted by short-term hype, true revolutions are built in the labs of Open Science. Today, the Qubic ecosystem reached a historic milestone that bridges the gap between theoretical mathematics and decentralized Artificial General Intelligence (AGI).
🏆 The Academic Breakthrough in Osaka
We are thrilled to announce that researchers Jose Sanchez and David Vivancos have had their groundbreaking paper, "The Neutral Buffer State: Trinary Logic Advantage in Branching Ratio Stability for Continuous-Time Networks," officially accepted for publication and presentation at the AMLDS 2026 International Conference in Osaka, Japan.
This is not just another "crypto update." This is a peer-reviewed validation supported by prestigious institutions including IEEE, SMC, Kansai University, and NICT.
🧠 Why "Trinary Logic" Changes Everything
The core of the paper focuses on the Trinary Logic Advantage. Most modern AI is built on binary systems (0s and 1s), which are fundamentally limited in simulating the complexity of the human brain.
Qubic’s approach—utilizing Trinary Logic—allows for:
Branching Ratio Stability: Ensuring that neural signals in continuous-time networks remain stable and efficient.Bio-inspired Intelligence: Moving away from rigid code toward a system that mimics biological neural dynamics.True AI: Creating the mathematical foundation for intelligence that can actually "think" and evolve, rather than just predict the next word in a sequence.
🏗️ The Foundation of Neuraxon & Aigarth
This research is an integral part of the development of Neuraxon and Aigarth for the Qubic network. By grounding these projects in rigorous scientific research, Qubic is distancing itself from the "black box" models of centralized AI.
Through Open Science, the work led by @c___f___b ensures that the path to AGI remains transparent, decentralized, and mathematically superior.
💡 Final Thoughts for Investors
In a market saturated with "AI wrappers," Qubic is building the Native AI Infrastructure. When world-class academic conferences like AMLDS recognize the validity of Trinary Logic in neural networks, it sends a clear signal:
The future of AGI is not Binary. It is Trinary. And it is being built on Qubic.
#Qubic #Neuraxon #aigarth #OpenScience #trinary

While the world is obsessed with "Blockchain vs. Traditional Finance," a deeper revolution is happening under the hood of Qubic. It isn't just another ledger; it is a fundamental redesign of how computers process information. By abandoning the 80-year-old Binary standard for Balanced Ternary logic, Qubic has unlocked a level of efficiency that Big Tech is only beginning to fathom.
1. The Visionary Rebellion: "Trust Math, Not CEOs"
The Qubic story began in April 2022, led by Sergey Ivancheglo (CFB). His solution to achieve true AGI? Bare-metal programming. Qubic interacts directly with the hardware, stripping away the bloat of operating systems. This ensures zero latency and maximum throughput, allowing the network to reach a theoretical 15 million Transactions Per Second (TPS).
2. The Ternary Advantage: Why 3 is Greater than 2
Most computers think in Bits (0 or 1). Qubic thinks in Trits (-1, 0, +1). This is known as Balanced Ternary, and it is the "secret sauce" of Qubic’s superiority.
A. Mathematical Efficiency (Radix Economy)
In computer science, the most efficient base for a numbering system is approximately 2.718 (Euler's number).
Binary (Base 2): Distance from Euler's number is ~0.718.Ternary (Base 3): Distance from Euler's number is only ~0.282.
Because 3 is closer to 2.718 than 2 is, Ternary systems are theoretically 15% more efficient at processing information. Qubic does more with less energy—a critical factor for global-scale AI training.
B. The "Native Language" of AI
Biological neurons operate on three states: Inhibition (-1), Rest (0), and Excitation (+1).
Traditional AI (OpenAI, Google) must simulate these states using Binary (0 and 1), wasting massive compute power. Qubic’s Aigarth system uses Ternary logic natively. It "speaks" the same language as a biological brain.
3. The 2026 Breakthrough: Neuraxon 2.0
Qubic hasn't just talked about this vision; it has built it:
Neuraxon 2.0 (March 2026): The release of a 1.1TB neural dynamics dataset on Hugging Face.375.5 Million Synapses: A 24x growth in complexity, proving that the uPoW (Useful Proof of Work) engine is successfully generating high-fidelity artificial life.
4. Qubic vs. The World: A Technical Comparison

5. The Convergence: AI + Finance + Math
We are entering the era of Convergence. Qubic’s uPoW doesn't just secure the network; it fuels the evolution of Aigarth. The data generated (Neuraxon 2.0) is then used to refine the AI, which in turn makes the network's smart contracts more intelligent.
As financial hubs like Hong Kong push for "AI+ Finance," Qubic stands ready with a bare-metal, ternary-powered foundation that is built for the next century of computing.
Conclusion
Sergey Ivancheglo didn't build a better Bitcoin; he built a different type of intelligence. By choosing the mathematical purity of Ternary over the convenience of Binary, Qubic has positioned itself as the only decentralized network capable of hosting true AGI.
The pieces were impressive. The system is unprecedented. Welcome to Qubic.
#Qubic #bitcoin #UPoW #sha256 #trinary

This is a pivotal moment for the AI industry. Sam Altman’s announcement of OpenAI’s partnership with the Department of War (DoW) marks the official birth of the "AI-Military Complex."While the narrative focuses on "safety" and "responsibility," for those in the decentralized space, this is the ultimate signal. We are witnessing the bifurcation of intelligence: State-Controlled Centralized AI vs. Network-Owned Decentralized AI.Here is a deep-dive analysis of why this move by OpenAI makes the mission of Qubic more critical than ever.
The AI-Military Complex: Why Decentralized Intelligence (Qubic) is the Only "Safe" Path Forward
Sam Altman recently confirmed that OpenAI has reached an agreement to deploy its models within the classified networks of the Department of War. While the post emphasizes "prohibitions on mass surveillance" and "human responsibility," it raises a fundamental question: Can a "Black Box" owned by a centralized entity and governed by a state department truly be "safe" for the rest of humanity?
1. The Illusion of Centralized Safeguards
Altman mentions "technical safeguards" and "FDEs" (Full Disk Encryption) to ensure models behave. However, in a centralized architecture, these safeguards are just code written by humans under political pressure.
The Qubic Alternative: Qubic’s Aigarth doesn't rely on brittle, manually-coded safeguards. It utilizes Evolutionary AI. Through Useful Proof of Work (uPoW), the network’s neural systems (Neuraxon v2.0) evolve based on mathematical efficiency and natural selection. You don't "program" it to be good; it evolves to be optimal within the transparency of a decentralized Layer 1.
2. Sovereignty vs. Classified Firewalls
OpenAI is moving into "classified networks" and "cloud networks only." This creates a massive single point of failure and a lack of transparency. If the intelligence that runs our world is hidden behind a military firewall, who does it actually serve?
The Qubic Alternative: Qubic is governed by 676 Quorum Members, operating on a borderless, public infrastructure. With a verified 15.52 Million TPS, Qubic provides the "High-Frequency Tick" required for global AGI without needing a centralized cloud. It is an Universal Compute Engine that belongs to the holders and the miners, not a government agency.
3. The Ethics of Compute: War vs. Evolution
Altman acknowledges the world is a "dangerous place." Centralized AI labs are now optimizing for "velocity and breakthroughs" in a winner-take-all race, often leading to the human burnout we saw with engineers like Hieu Pham.
The Qubic Alternative: Instead of directing compute power toward classified military outcomes, Qubic’s uPoW repurposes mining energy to solve the "Compute Bottleneck." We are turning the energy used for network security into the very energy that trains Intelligent Tissue. It is a shift from the destruction of resources to the evolution of intelligence.
The Equilibrium: The Choice is Ours
The partnership between OpenAI and the DoW proves that centralized AI will eventually be absorbed by the state. This is the "Equilibrium" we were warned about.
However, there is another path. A path where:
Privacy is baked into the protocol.Trust is found in decentralized consensus, not classified agreements.Evolution outpaces human-coded bias.
The "Universal Compute Engine" isn't just a technical milestone; it is a necessity for a free future. As the world becomes more "complicated and messy," the need for a decentralized, neutral, and high-performance AI infrastructure like Qubic has never been more urgent.
The future is not being programmed in a lab; it is being evolved on the network.
#Qubic #OpenAI #SamAltman #UPoW #Neuraxon

Chào cộng đồng @Binance Vietnam ! Là một người mới gia nhập đại gia đình Binance Square, tôi rất hào hứng khi được chia sẻ góc nhìn chuyên sâu của mình về xu hướng bùng nổ nhất hiện nay: Sự giao thoa giữa Trí tuệ nhân tạo (AI) và Blockchain. Trong bài viết đầu tiên thuộc chiến dịch #CreatorpadVN này, chúng ta sẽ cùng phân tích bệ phóng mà $BNB đang tạo ra cho kỷ nguyên AI.

1. Bước tiến chiến lược của BNB Chain với MCP
Mới đây, hệ sinh thái BNB Chain đã thực hiện một bước đi đột phá khi hỗ trợ các AI Agent thông qua giao thức MCP (Model Context Protocol). Giờ đây, các đặc vụ AI không còn là những dòng code vô tri, chúng đã được trang bị các "kỹ năng" thực thụ:
Đọc dữ liệu chuỗi: AI có thể phân tích hợp đồng thông minh và dữ liệu on-chain theo thời gian thực.Thực thi giao dịch: AI có khả năng quản lý ví và tự động thực hiện các lệnh swap hoặc chuyển tiền.Định danh On-chain: Với tiêu chuẩn ERC-8004, mỗi AI Agent sẽ có một danh tính riêng biệt trên mạng lưới.
Điều này biến $BNB trở thành một lớp thanh toán và thực thi lý tưởng cho các ứng dụng AI hiện đại.
2. Từ AI "Lắp ghép" đến AGI "Bản địa"
Trong khi BNB Chain cung cấp công cụ kết nối tuyệt vời, thế giới khoa học cũng đang chứng kiến những bước tiến khổng lồ về hạ tầng AI lõi. Một minh chứng điển hình là bài báo khoa học "The Neutral Buffer State: Trinary Logic Advantage in Branching Ratio Stability for Continuous-Time Networks" vừa chính thức được chấp nhận để công bố và trình bày tại Hội nghị Quốc tế AMLDS 2026 tại Osaka, Nhật Bản.
Đây là một phần thiết yếu trong nghiên cứu về Neuraxon và Aigarth. Việc sử dụng Logic Tam phân (Trinary Logic) thay vì nhị phân truyền thống giúp mô phỏng não bộ sinh học chính xác hơn, tạo tiền đề cho sự ra đời của AGI (Trí tuệ nhân tạo tổng quát) thực thụ.
3. Tầm nhìn 2026: Khi BNB Hội Ngộ cùng AGI
Sự kết hợp giữa một blockchain mạnh mẽ, phí rẻ như BNB Chain và những đột phá về AI mã nguồn mở (Open Science) sẽ tạo ra một hệ sinh thái không thể ngăn cản. Binance không chỉ là nơi giao dịch, mà còn là bệ phóng cho những công nghệ thay đổi nhân loại.
Hãy cùng tôi cập nhật những xu hướng công nghệ mới nhất và tận dụng cơ hội từ hệ sinh thái Binance ngay hôm nay!
👉 Khám phá ngay tại: Beyond Binary
#CreatorpadVN #Binance #Qubic #BinanceVietnam

Before leaving, Hieu pointed out a profound truth about the future of AI: Currently, there are only two bottlenecks left—Compute and Humans.
Silicon Valley’s centralized model is hitting a massive wall on both fronts. So, what happens when the smartest minds burn out and centralized servers reach their physical limits? The paradigm has to shift. This is exactly where Qubic and its Universal Compute Engine step in to change the game.
Solving the "Compute" and "Human" Bottleneck
While centralized giants are squeezing their engineers, the decentralized world is quietly building an infrastructure that scales without human suffering or wasted energy. Here is why the crypto and AI communities are suddenly looking at Qubic:
uPoW (Useful Proof of Work) vs. The Human Bottleneck: Instead of relying on exhausted engineers to manually code every breakthrough, Qubic is powering Aigarth—a system designed to find new paradigms for AI creation. uPoW utilizes global CPU power to train artificial neural networks (ANNA). The goal is recursive self-improvement, shifting the heavy lifting from human developers to the network itself.The Dogecoin Mining Revolution: Qubic is not just theorizing; it is executing. Starting April 1st, Qubic will introduce parallel mining. ASICs will secure the Dogecoin network, while CPUs simultaneously train AI on Qubic. Same network, same energy, dual rewards. We are turning meme-coin energy into AGI brainpower.Oracle Machines in Action: How does Qubic validate external events like Doge mining shares? Through its built-in Oracle Machines. This isn't just a concept; the Oracle system went live on mainnet in February and has already seamlessly processed over 11,000 queries.15.52M TPS (CertiK Verified): To run a global AI training mechanism alongside Doge mining and Oracle validation, you need unprecedented speed. Qubic’s Layer 1 operates at a staggering 15.52 million Transactions Per Second. This isn't just for financial transfers; it is the high-frequency "tick" rate required to synchronize a global, decentralized brain.
The Inevitable Shift
Hieu Pham’s departure is a wake-up call. The centralized, closed-door race to AGI is unsustainable. The future of intelligence cannot be built on the ashes of burnt-out engineers and centralized server monopolies.
It will be built on decentralized networks where compute is shared, energy is repurposed, and intelligence evolves organically. The dawn of the Universal Compute Engine is here. Are you paying attention?
#Qubic #OpenAI #XAI

Introduction: A Shift in Understanding
In the world of Qubic, we often hear terms like AI, ANNA, and Aigarth used interchangeably. However, according to CFB’s vision, we must look deeper. If the current AI industry is building "tools," Qubic is building a new Paradigm. As CFB famously stated: "Aigarth is not AI; it’s a project aiming to find new paradigms for the creation of AI."
1. ANNA: The Living Neural Engine
ANNA (Artificial Neural Network Assembly) is the raw, evolving intelligence within the Qubic ecosystem. It is the active force trained by the global network through uPoW (Useful Proof of Work).
The Actor: Unlike a static database, ANNA is the active intelligence that can execute, learn, and eventually act. As CFB pointed out, while AI agents can "deploy contracts," Aigarth itself serves a different purpose.
2. Aigarth: The "Book" of Universal Patterns
If ANNA is the brain, then Aigarth is the Library. * Not an Agent, but a Paradigm: Aigarth is not a chatbot or a functional AI agent. It is a repository of discovered logic and patterns. CFB describes it as a "Book"—a collection of wisdom and instructions that define how intelligence should be structured.
A Blueprint for Creation: The goal of the Aigarth project is to move away from the "black box" of modern deep learning and find a transparent, trinary-based logic for creating AI that is more efficient and truly decentralized.
3. The 15.52M TPS Infrastructure: Why It Matters
To write this "Book" of intelligence, you need a massive, high-speed recording system.
The 15.52 million TPS (verified by CertiK on 22/04/2025) isn't just for financial transactions. It provides the high-frequency "ticks" necessary for the network to synchronize complex neural updates.In this ecosystem, speed equals the resolution of the "Book." Higher throughput allows for more complex paradigms to be discovered and recorded within Aigarth.
4. The Countdown to April 13, 2027
The "launch" of AiGarth on 13/04/2027 is not the release of a product, but the completion of a foundational phase.
It is the day the "Book" becomes readable for external developers and AI agents.It marks the moment when the world can use the paradigms discovered within Aigarth to create AI that is censorship-resistant, zero-fee, and truly autonomous.
Conclusion: The Future is Decentralized Creation
We aren't just waiting for a smarter Siri. We are waiting for a new way to create intelligence. Aigarth is the vessel, ANNA is the spark, and Qubic is the furnace. On April 13, 2027, the "Book" opens, and the era of centralized AI monopolies ends.
#Qubic #AiGarth #Anna

The most anticipated milestone of 2026 for the $QUBIC ecosystem has been unveiled: Dogecoin ($DOGE ) mining is officially coming to the Qubic network. This is not just another integration; it is a massive leap toward turning Qubic into a truly Universal Compute Engine.
📍 Current Status: Where We Stand
✅ Design Phase: Completed.✅ Project Plan: Finalized.⚙️ In Progress: Two workstreams are running in parallel—Technical implementation (Computor coordination) and Business planning (Community proposal).
📅 Key Milestone:
Target Mainnet Launch: April 1, 2026.
💡 Why This Changes the Game
Evolution of Useful Proof of Work (uPoW): After successfully proving the concept with Monero ($XMR), Qubic is taking it a step further. We are proving that the energy used to secure a network and train AI can simultaneously mine external top-tier assets.Maximum Energy Utility: Miners securing the Qubic network and training its AGI "brain" will now be able to mine $DOGE — one of the world's most recognized cryptocurrencies. This means more value from the same energy and more liquidity flowing through the Qubic infrastructure.Massive Community Synergy: Dogecoin boasts one of the largest and most active communities in crypto. Bridging that energy with Qubic’s advanced compute layer opens doors that go far beyond a simple mining integration.
🔍 The Bottom Line:
Qubic is delivering on its promise: No energy is wasted. Instead of calculating useless hashes, we are building the future of AGI and being rewarded with real-world assets.
If Qubic can mine the "King of Memes" while training AI, the argument for Useful Proof of Work as the future of all mining becomes undeniable.
Stay tuned. April 1st marks a new chapter for decentralized AI and mining efficiency.
Hashtags: #Qubic #DOGECOİN #uPoW

As of January 14, 2026, contracts now pay for the computational resources they actually consume.
The update was first validated in a live testnet environment, then rolled out to mainnet, introducing organic burn directly proportional to the work a contract performs.
Why Execution Fees Matter
Every smart contract on Qubic maintains an execution fee reserve, essentially a prepaid balance that covers its compute costs.
When that reserve is depleted, the contract doesn’t disappear, but it does go dormant. It can still receive funds and respond to basic system events, however its core functions can’t be called again until the reserve is replenished.
Previously, a contract only needed a positive balance to remain active. The system verified that a reserve existed, but execution costs were not deducted based on actual computation. That has now changed. Contracts are charged proportionally to how long their procedures take to execute, aligning fees directly with real computational work.
How the System Works
The fee mechanism operates in phases, each lasting 676 ticks. Here's the process:
Execution and Measurement: When computors run your contract's procedures, they measure how long each execution takes.
Accumulation: These measurements build up over a complete 676-tick phase.
Consensus: Computors share their measured values through special transactions. The network aggregates these reports and uses the  two-thirds percentile to determine a fair, agreed-upon execution fee.
Deduction: The consensus fee gets subtracted from the contract's reserve in the following phase. This phase-based approach keeps consensus efficient while ensuring accuracy across the network.
Phase n-1          Phase n              Phase n+1
(676 ticks)        (676 ticks)          (676 ticks)
    │                  │                    │
    └── Fees computed ─┘── Fees deducted ───┘
Who Pays for What
The system follows a simple principle: whoever initiates an action pays for it. When a user calls a contract procedure, that contract's reserve covers the cost. When Contract A calls Contract B, Contract B's reserve gets checked before execution proceeds.
Some operations remain free of execution fee checks:
Operation
Fee Check
User procedure calls
Yes
Contract-to-contract procedures
Yes
Contract-to-contract functions
Yes
System callbacks (transfers, etc.)
No
Read-only functions
No
Epoch transitions
No
Functions that only read data never cost anything. They provide access to contract state without modification, so they run regardless of reserve status. For more details on how procedures and functions differ, see the QPI documentation.
What Builders Should Do
If you maintain a smart contract on Qubic, consider these steps:
Review your reserve status. Check contracts.qubic.tools to see current fee consumption for your contract based on execution patterns. You can also monitor contract activity through the Qubic Explorer.
Examine your procedures. Code that returns early, uses fewer resources. Procedures that loop excessively or repeat redundant operations will cost more.
Plan for sustainability. Contracts can replenish their reserves through the qpi.burn() function or through QUtil's BurnQubicForContract procedure. You can execute these operations using the Qubic CLI. It is recommended that you ensure your contract includes a reliable mechanism for maintaining adequate reserves throughout its lifecycle.
Handle errors gracefully. When calling other contracts, check whether those calls succeeded. If a target contract has insufficient fees, your call will fail and return an error code. Build in fallback logic where appropriate.
For developers new to building on Qubic, the smart contract development guide provides a solid starting point.
What Computors Should Know
Computors have a new configuration option: the execution fee multiplier. This setting converts raw execution time into fee amounts. The network reaches consensus using the two-thirds percentile of all computor-submitted values, preventing any single operator from dramatically shifting costs.
For more information about running a computor, refer to the computor documentation.
Refilling Reserves
Three methods exist for adding to a contract's execution fee reserve:
Internal burning: Contracts can call qpi.burn(amount) to convert collected fees into reserve balance. They can also fund other contracts using qpi.burn(amount, targetContractIndex).
External contributions: Anyone can send funds to the QUtil contract's BurnQubicForContract procedure, specifying which contract should receive the reserve boost.
Legacy method: QUtil's BurnQubic procedure adds specifically to QUtil's own reserve.
These mechanisms tie directly into Qubic's tokenomics, where burning serves as the core deflationary mechanism rather than traditional transaction fees.
Protection for Users
The system includes built-in safeguards. If you send a transaction to a contract with depleted reserves, any attached funds are automatically returned. You won’t lose money because a contract failed to maintain its balance.
Read-only queries remain available even for dormant contracts. You can check its state at any time, but state-changing procedures won’t run until the reserve is replenished.
What This Means for Qubic
This update marks a meaningful shift in how Qubic handles smart contract economics. 
Contracts that perform more work, pay more. Efficient code becomes genuinely valuable. And the network gains a sustainable mechanism for burning tokens tied to actual utility rather than arbitrary fixed amounts.
If you build on Qubic and haven't yet reviewed your contracts under this new model, now is the time. For technical details, see the full reference documentation on GitHub.
Join the Qubic Discord or Telegram community to ask questions, share ideas, and discuss implementation strategies with other builders.or Telegram community to ask questions, share ideas, and discuss implementation strategies with other builders.
#Qubic #SmartContracts

Written by Qubic Scientific Team

How does a neuron function over time?
Biological neurons do not function like a bedroom light switch being turned on. They are a continuous dynamic system. The neuronal state evolves constantly, even in the absence of external stimuli.
How does a neuron function over time?
Basically, by moving electrical charges (ions) in or out of its membrane, that is, by changing its electrical potential. Ions enter or leave (mainly sodium and potassium) through the different gates of the neuron with a certain intensity, modifying the potential. There are some gates, called leakage gates, where ions are always entering and leaving.
Time is implicit. The electrical potential changes constantly, over time.
The change in a neuron’s electrical potential over time depends on:
The external current applied + the balance between the flows of sodium ions (which increase it) and potassium ions (which decrease it) through the gates that open and close.
Don’t panic with the graph. Positive and negative electrical charges (ions) flow through the gates causing depolarization (so current moves along to the end of the neuron) or hiperpolarization (so it comes back to a neutral state). 

The potential (V) changes over time, that is mathematically, dV/dt, as a function of the sum of the input and output gates.
This is the fundamental model of computational neuroscience, which expresses that the state of the neuron depends both on current signals and on its immediate history. There is no “reset” between events, since each stimulus falls onto a system that is always running.
Now let’s move to Neuraxon, which is a bio-inspired model.

We want it to be alive, an intelligent tissue. It cannot have discrete states, but continuous ones.
In Neuraxon, instead of ion gates that open and close and move charges with a certain intensity, changing voltage, we have dynamic synaptic weights. But the model equation maintains a clear and direct similarity with the biological neuron.
What does this mean?
Instead of V, voltage in biological neuron, the state of Neuraxon, is s. And it changes over time too, therefore ds/dt is a function of the weights and activations and the previous state.
Unlike a classical AI model, where the synaptic weights of a network represent stereotyped outputs to an input, in Neuraxon the weights are not static.
Imagine, for example, an “email inbox” automatic response mechanism.
In classical AI, the rule does not adjust or change over time or context.
In Neuraxon, it is taken into account whether the “email input” comes from the same person (which could indicate urgency) or whether it arrives on a weekend (which may generate a no-response output). In other words, the rule remains, but when and how the response is given is modulated.
Do LLMs compute time?

Large language models appear to show deep understanding in many contexts, but they operate under a logic different from biological systems (Vaswany, 2017). They do not function based on an internal temporal dynamic, on a “change in potential” or on “synaptic weights” that modulate response, but rather process discrete sequences.
In LLMs, “time” does not exist, which makes it difficult for them to simulate biological behavior (such as intelligence). LLMs know how to distinguish which word comes before and which comes after, but they do not grant an experience of duration or persistence. Order replaces time.
Unlike Neuraxon, they do not possess internal rhythms that speed up or slow down, nor do they show progressive habituation to repeated stimuli, nor can they dynamically anticipate based on an internal state that changes over time.
The LLM model computation would be something like:
output = Fθ(input)
so outcomes are fixed solutions from a function (combination) of inputs.
There is no state as a function of time. These are data that form huge matrices and change their value through a specific function, which, as in the example cited, restricts the possibilities: email input → automatic response.
Wrapping up. The distance between bio-inspired models such as Neuraxon and large language models should not be explained in terms of computational power or data volume. There is a deeper difference.
The brain is, in itself, a continuous temporal system. Its functioning is defined by dynamics that unfold over time, by states that evolve, decay, and reorganize permanently, even in the absence of external stimuli (Deco et al., 2009; Northoff, 2018).
Neuraxon deliberately positions itself within that same logic. It does not attempt to imitate 1 to 1 the biophysical complexity of the brain, but it explicitly incorporates time as a computational variable. Its internal state evolves continuously, carries the past, and modulates the present, allowing adaptation without the need for a reset.
LLMs, by contrast, operate very differently. They manipulate symbols ordered in discrete sequences without their own temporal dynamics. There is no time, only order. There is no adaptation, only pre-defined responses.
As long as time does not form part of the state governing computation, LLMs may be effective, but they will hardly be autonomous in a strong sense.
Future artificial intelligence aims to operate in dynamic environments. This is the reason why Neuraxon includes time as a fundamental variable.
A living intelligence tissue…
How This Relates back to Qubic?
Qubic provides the continuously running, stateful computational environment required for time-aware intelligence.
It is the natural substrate on which models like Neuraxon - adaptive, persistent, and never “resetting” - can exist and evolve.
Addenda
Take a look at the equations. Don´t panic!
1 Biological neuron, V potencial, “sum of gates flux in & out”

2 Neuraxon model equation - clear and direct similarity with the biological neuron.
s state, wi & f(si) dynamic synaptic weights 

3 LLM model equation. Inputs (ordered in a matrix) create matrix outputs through a fixed function 
p (xn+1 | x₁, …, xn) = softmax (Fθ (x₁, …, xn) )

References
Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. J. (2009). The dynamic brain. PLoS Computational Biology, 5(8), e1000092.Northoff, G. (2018). The spontaneous brain. MIT Press.Vaswani, A., et al. (2017). Attention is all you need. NeurIPS.Vivancos, D., & Sanchez, J. (2025). Neuraxon: A new neural growth & computation blueprint. Qubic Science.rint. Qubic Science.
#Qubic #Neuraxon

Analysis of Informing deep neural networks by multiscale principles
In the brain, neuromodulation is the set of mechanisms through which certain neurotransmitters modify the functional properties of neurons and synapses, altering how they respond, for how long they integrate information, and under what conditions they change with experience.
These effects are produced mainly through neurotransmitters such as dopamine, serotonin, noradrenaline, and acetylcholine, which act on receptors known as metabotropic receptors. Unlike fast receptors, these do not directly generate an electrical signal, but instead activate cellular signaling pathways that modify the dynamic regime of the neuron and the circuit.
The article by Mei, Muller, and Ramaswamy published in Trends in Neurosciences starts from a well-known limitation of deep neural networks. These networks learn well in stable environments but adapt poorly when tasks change.
In response to this, the authors ask whether a bio-inspired model with neuromodulation could make artificial networks more adaptive. The central idea is to introduce signals that do not represent information about the environment, but that do regulate how the network learns, emulating how dopamine, serotonin, and others operate in biological systems.

Dynamic Learning Rate: A Bio-Inspired Approach to Adaptive Neural Networks
In deep neural networks, for learning to occur, the learning rate must be taken into account. This is the parameter that determines how much the weights of the network are modified when an error is made. A high value allows fast learning but makes the system unstable; a low value makes learning slow but more conservative.
We can see this with an example: Imagine that a very simple artificial neural network has a single weight that is used to decide whether an image contains a cat or not. That weight initially has a value of 0.5. The network makes a prediction, gets it wrong, and the algorithm calculates that in order to reduce the error that weight should decrease.
The key question is how much it should change, and that is determined by the learning rate. If the learning rate is high (for example 0.5), the adjustment is large: the weight can go from 0.5 to 0.0 in a single step. The network learns quickly, but these abrupt changes cause the weight to oscillate if subsequent examples push in opposite directions, making learning unstable.
If the learning rate is low (for example 0.01), the same error produces a small change and the weight goes from 0.5 to 0.49. The network learns more slowly, but in a progressive and stable way. In classical deep neural networks, this value is fixed before training begins and remains constant throughout the entire process, so the network always learns at the same speed.
Faced with such rigid learning, the article proposes that, analogously to brain neuromodulation, this parameter could vary dynamically depending on context, increasing in response to novelty or decreasing when stability is needed.
Dropout Regularization: A Simplified Model of Neural Variability
Another relevant concept they address is dropout. Imagine a neural network that always uses the same set of neurons to solve a task. Over time, the network becomes very efficient along that specific path, but also very fragile, because if that path stops working, performance drops sharply.
Dropout introduces a simple solution to this problem. During training, some neurons are randomly switched off, forcing the network to search for alternative routes to reach the result. In this way, the network learns to distribute information and becomes more robust.
The article interprets this mechanism as a very simplified equivalent of neuromodulation, since it does not change the content being processed, but rather which parts of the network participate at each moment, favoring more exploratory or more stable states depending on the need, although in a fixed way and without true contextual dynamics. The brain truly operates this way; it does not repeat activation patterns either over time or across space. There is variability that favors flexible behavior.
Modulated Synaptic Plasticity: Beyond Automatic Weight Updates
The article also discusses a third mechanism: modulated synaptic plasticity, that is, when and under what conditions the network's weights change in a lasting way.
In classical deep neural networks, plasticity is automatic because every error produces a weight update. However, in biological systems, the coincidence of activity between neurons does not guarantee learning. For a connection to strengthen or weaken, a specific neuromodulatory state is required, as if it were a signal of permission or veto.
The authors introduce a modulatory signal that authorizes or blocks weight updates, thus conditioning plasticity rather than the calculation of the error. The results of these approaches show measurable improvements in sequential learning and a reduction in catastrophic forgetting of previous tasks.
Limitations of Bio-Inspired Deep Learning in Atemporal Architectures
However, all of these advances occur within architectures that remain essentially atemporal. In the brain, neuromodulation acts on systems whose activity unfolds over time. In most deep neural networks, time is not part of the internal computation; it exists only as an external framework. The network is trained step by step, but during inference there are no states that evolve.
Therefore, although the authors introduce neuromodulation, what they actually do is adjust parameters of a static system, not modulate a living process.
In Transformers, this limitation is even greater. The attention mechanism is a mathematical operation that assigns relative weights to different parts of an input. It serves to decide which information is more relevant, but it does not introduce persistence or transitions between states. Time is symbolic, not dynamic.
For this reason, in bio-inspired neuromodulation of Transformers, what is really being done is the modulation of combinations of representations. There is no tonic activation, no latency, no learning during inference. Performance is improved, but the functional role it has in the brain is not reproduced.
Neuromodulation in Neuraxon: A Truly Dynamic Approach to Bio-Inspired AI
Neuraxon starts from a different premise. In its basic design, computation occurs in time, not over time. The system maintains internal states that evolve, even in the absence of clear external stimuli. Subthreshold activity, persistence, and transitions between states are part of the computation.
In this context, neuromodulation is not implemented as an external adjustment of parameters such as the learning rate or dropout, but as a direct modulation of internal dynamics.
Modulators influence how activity propagates, which patterns stabilize, and under what conditions the system becomes more plastic or more conservative.
The comparison with the brain is evident. We cannot reproduce cerebral complexity with thousands of receptors and an extremely complex anatomy. But what is preserved is what is essential, namely the dynamics of states. Learning occurs during the system's own operation, without separating training and inference.
In this sense, Neuraxon - Aigarth behave more like living systems than like networks trained in batches. Bio-inspired neuromodulation is part of the system, not a mathematical optimization mechanism. This aligns with Qubic's vision of decentralized AI built on Useful Proof of Work, where computing resources contribute meaningfully to AI training rather than arbitrary calculations.
References
Mei, J., Muller, E., & Ramaswamy, S. (2022). Informing deep neural networks by multiscale principles of neuromodulatory systems. Trends in Neurosciences, 45(3), 237-250.Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929-1958.Kirkpatrick, J., et al. (2017). Overcoming catastrophic forgetting in neural networks. PNAS, 114(13), 3521-3526.Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control: rewarding, aversive, and alerting. Neuron, 68(5), 815-834.Glimcher, P. W. (2011). Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis. PNAS, 108(Supplement 3), 15647-15654.Schmidgall, S., et al. (2024). Brain-inspired learning in artificial neural networks: A review. APL Machine Learning, 2(2), 021501.Vaswani, A., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30.
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