Luck3333

Ekosistem crypto Trump sedang mendapat serangan tetapi melawan balik! Berikut yang terjadi dalam 48 jam terakhir:
🛡️ 1. Serangan Stablecoin USD1 Dihentikan
World Liberty Financial (WLFI) baru saja selamat dari serangan media sosial terpadu. Peretas mengambil alih akun pendiri untuk menyebarkan FUD dan menjual short stablecoin USD1.
Hasil: USD1 sempat turun ke $0.997 tetapi langsung kembali. Dana aman (SAFU).
🏦 2. "World Liberty Trust" Sedang Datang
Trump tidak mundur. $WLFI telah resmi mengajukan permohonan ke OCC untuk mendirikan Perusahaan World Liberty Trust. Langkah ini bertujuan untuk membawa layanan stablecoin mereka langsung ke dalam sistem perbankan nasional AS. Besar untuk adopsi!

Pengantar: Perubahan dalam Pemahaman
Di dunia Qubic, kami sering mendengar istilah seperti AI, ANNA, dan Aigarth digunakan secara bergantian. Namun, menurut visi CFB, kita harus melihat lebih dalam. Jika industri AI saat ini sedang membangun "alat," Qubic sedang membangun Paradigma baru. Seperti yang dinyatakan CFB dengan terkenal: "Aigarth bukan AI; itu adalah proyek yang bertujuan untuk menemukan paradigma baru untuk penciptaan AI."
1. ANNA: Mesin Saraf Hidup
ANNA (Artificial Neural Network Assembly) adalah kecerdasan mentah yang berkembang dalam ekosistem Qubic. Ini adalah kekuatan aktif yang dilatih oleh jaringan global melalui uPoW (Bukti Kerja Berguna).

Abstract
The transition from predictive text models (Large Language Models) to Artificial General Intelligence (AGI) requires a fundamental shift from disembodied algorithms to physically interactive entities. The recent experiment by David Vivancos and Dr. José Sánchez—transplanting the Neuraxon v2.0 bio-inspired brain into a Sphero Mini robot—marks a critical milestone in #AliveAI. This article breaks down the theoretical framework, the hardware architecture, and the methodology for replicating this experiment.
1. The Theoretical Framework: Trinary Logic and Neural Growth
Traditional Artificial Neural Networks (ANNs) operate on binary logic, using continuous calculations that ultimately simulate "on/off" states. Neuraxon v2.0 completely departs from this by utilizing Trinary Logic, a system specifically designed to mimic the biological reality of human synapses.
In a biological brain, neurons do not merely excite one another; they also actively suppress signals to filter out noise and focus on important tasks. Neuraxon introduces this explicit third state: Inhibition. In this system, every artificial neuron can exist in one of three distinct conditions:
Excitation (+1): Actively passing the signal forward.Rest (0): Remaining neutral and conserving energy.Inhibition (-1): Actively blocking or suppressing the signal path.
How does a neuron make a decision? Instead of relying on rigid, pre-programmed rules, each Neuraxon neuron calculates a "weighted score" based on all incoming signals from its neighbors. If this combined signal is strong enough and surpasses a specific activation threshold, the neuron fires an Excitatory signal. If the incoming signals are overwhelmingly suppressive, it fires an Inhibitory signal. Otherwise, it stays at Rest.
Unlike static LLMs, Neuraxon employs a "Neural Growth Blueprint." This means the "weight" or importance of these connections physically alters its own network topology based on real-world feedback. When the Sphero Mini robot hits a wall, the negative physical feedback literally rewires the network's connections for the next attempt.
2. Hardware Architecture: Why the Sphero Mini?
To test physical cognition, the AI requires a "body" with sensory input and motor output. The Sphero Mini, despite its accessible ~$50 price point, serves as a perfect minimally viable organism.
It is equipped with an Inertial Measurement Unit (IMU), which is crucial for the AI to understand physics (gravity, momentum, and spatial orientation).
Sensory Input (Afferent Pathways): The 3-axis gyroscope and 3-axis accelerometer feed real-time spatial data back to the Neuraxon brain.Motor Output (Efferent Pathways): The AI calculates the required trinary signals to drive the internal dual-motor system, dictating speed and heading.
3. Experimental Methodology: Replicating the Setup
For researchers looking to experiment with open-science #AliveAI, the protocol is straightforward:
Step 1: Hardware Preparation
Acquire a Sphero Mini robot. Ensure it is fully charged and Bluetooth is enabled on your host machine.
Step 2: Access the Neuraxon Brain Interfaces
Navigate to the open-source Hugging Face spaces provided by David Vivancos:
For Locomotion (Neuraxon2MiniControl): This interface acts as the motor cortex, allowing you to observe how the neural network calculates basic navigation paths based on spatial input.For Fine Motor Skills (Neuraxon2MiniWrite): This requires higher-level cognitive processing. The AI must calculate the exact physical trajectories, accounting for physical friction and momentum, to draw specific letters or words on a surface.
Step 3: The Feedback Loop
Connect the Sphero to the interface via the Web Bluetooth API. Do not simply execute commands; observe the neural growth. When the Sphero attempts to write a letter, monitor how the Neuraxon code (available on GitHub) processes the physical drift and attempts to correct its trajectory in subsequent movements.
4. Analytical Implications
This experiment proves that intelligence cannot be fully realized in a vacuum. By forcing the AI to interact with physical laws, Qubic and the Vivancos team are building the foundational nervous system for future robotics. Today, it drives a sphere; tomorrow, this exact trinary, bio-inspired architecture could regulate the complex kinematics of a humanoid robot.
Key Takeaways: The Future of #AliveAi
From "Dead" to "Alive" AI: Moving beyond static Large Language Models (LLMs), Neuraxon v2.0 introduces embodied cognition, allowing AI to learn and adapt through real-world physical interaction and failure.Trinary Logic Superiority: By utilizing a -1 (Inhibit), 0 (Rest), and 1 (Excite) framework, Neuraxon mimics true biological brain efficiency, drastically reducing the computational waste seen in traditional binary systems.Accessible Open Science: The integration with a $50 Sphero Mini robot democratizes AI testing. It proves that developing physical AI doesn't require multi-million-dollar robotics labs.The Blueprint for AGI: Powered by the decentralized Qubic network, this "brain transplant" experiment lays the foundational nervous system for the complex kinematics of future humanoid robotics.
#Qubic #AGI
Neuraxon2MiniControl 👉https://huggingface.co/spaces/DavidVivancos/Neuraxon2MiniControl

Finality. A simple word that carries immense weight in the blockchain space. It’s the point where a transaction is locked, beyond tampering or doubt. For most blockchains, this isn’t as immediate or certain as one might think. Bitcoin? You’re counting six blocks deep, holding your breath. Solana? Validators have to agree and then execute - quick, but not instantaneous. But Qubic? Qubic redefines this process entirely, delivering instant finality in a system where forks don’t exist.
What Traditional Blockchains Get Wrong
In most blockchains, finality is far from straightforward. Forks - branching pathways created when competing chains temporarily exist - trap transactions in limbo. Users must wait for confirmations, wait for chain disputes to be resolved, and hope their transaction makes it onto the “main” branch. It’s a cumbersome process.
Take Bitcoin, for example. You send a transaction, but the process isn’t instantaneous. One block passes, then two, then six - only then does the transaction feel secure. Why? Because forks can override earlier blocks, potentially reversing your transaction.
Even faster systems like Solana aren’t immune to bottlenecks. Validators must reach agreement, a process that slows when network traffic surges or disagreements arise. The result? "Instant" finality that’s not quite instant.
Qubic’s Rewrite of Finality
Qubic’s approach is different. It eliminates forks entirely and slashes waiting times to virtually zero. Its tick-based architecture removes the inefficiencies baked into traditional blockchain designs.
Tick-Based Processing
Imagine time segmented into fixed, immutable slices called “ticks.” Each tick represents a brief processing interval. Transactions submitted during, for example, Tick 100 are evaluated and finalised by Tick 101. If your transaction is valid, it’s confirmed. If not, you’ll know immediately - it’s as simple as that. No waiting, no ambiguities.
A Forkless Highway
While traditional blockchains navigate complex branching paths, Qubic moves in a straight line. Transactions flow through a single, unbroken sequence, with no forks to manage or resolve. This streamlined approach eliminates the need for redundant confirmations.
Deterministic Finality
In Qubic, once a transaction is processed within its tick, the outcome - whether successful or not - is final. There’s no need for validators to reach additional agreements or for users to wait for multiple confirmations.
Clarifying Success and Finality
It’s important to note that Qubic guarantees the finality of valid transactions. However, not every transaction will succeed. If a transaction fails - perhaps due to insufficient funds, conflicting requests, or invalid inputs - the network will reject it during the tick processing, and you’ll know immediately. This transparency allows users to act quickly without the uncertainty of delayed rejections.
Why Does This Matter?
It’s not just about speed. It’s about confidence - knowing that what you send is final before the thought of doubt even creeps in.
Finance
Cross-border payments processed in under a second. No intermediaries, no reversals, just frictionless commerce. 
Gaming
Imagine in-game transactions - buying items, trading assets, earning rewards - all processed instantly. No lag, no waiting, no broken immersion.
Supply Chains
A factory ships a product. The transaction logs instantly, providing real-time visibility to suppliers, shippers, and buyers. The chain of custody is secure, final, and transparent.
A Walkthrough: Qubic’s Simplicity in Action
Let’s break it down. Say you’re sending $QUBIC coins:
At Tick 100, you initiate the transaction.By Tick 101, the network processes it. If it’s valid, it’s finalised. If not, you’ll know instantly why it failed.
The result? A seamless user experience.
The Forkless Advantage
Forks complicate things. They demand extra resources, complicate consensus, and inject uncertainty into every transaction. By eliminating forks entirely, Qubic reclaims all that wasted energy and delivers a system that:
Reduces wasted computational resources.Simplifies transaction flow.Delivers a seamless and reliable experience.
Ready to Explore the Next Evolution in Blockchain?
Qubic is making a statement. A statement that finality should be instantaneous, that confidence should be absolute, that innovation should never come at the cost of usability. 
Whether you’re a developer, gamer, or business leader, Qubic’s instant finality provides the reliability, speed, and confidence you need to build innovative systems. From high-speed financial applications to gaming platforms to supply chain systems, Qubic’s instant finality transforms the possibilities.
 Want to know more? Take a closer look and discover the future of blockchain technology.
 Join the Community on Discord and Telegram.
Explore Qubic Docs.

Pasar kripto berteriak minggu ini! Dengan Indeks Ketakutan & Keserakahan mencapai 7 yang mencekam (Ketakutan Ekstrem), udara dipenuhi dengan kepanikan. Tapi seperti pepatah lama: "Jadilah serakah saat orang lain takut." Apakah ini momen "Beli Saat Turun" yang ultimate atau jebakan sebelum kehancuran total? Mari kita selami kekacauan.
⚡ "Keadaan Persatuan" Rebound
Just ketika Bitcoin flirte dengan bencana di $63,000, pidato Kenegaraan Presiden Trump menyuntikkan semangat ke dalam grafik. BTC dan ETH melompat 3% secara instan saat pasar bertaruh pada kekuatan makro. Namun, "Trump Pump" menghadapi dinding besar perlawanan.

Mulai 14 Januari 2026, kontrak sekarang membayar untuk sumber daya komputasi yang sebenarnya mereka konsumsi.
Pembaruan pertama kali divalidasi di lingkungan testnet langsung, kemudian diluncurkan ke mainnet, memperkenalkan pembakaran organik yang sebanding dengan pekerjaan yang dilakukan kontrak.
Mengapa Biaya Eksekusi Penting
Setiap kontrak pintar di Qubic mempertahankan cadangan biaya eksekusi, pada dasarnya adalah saldo prabayar yang menutupi biaya komputasinya.
Ketika cadangan itu habis, kontrak tidak menghilang, tetapi menjadi tidak aktif. Kontrak tersebut masih dapat menerima dana dan merespons peristiwa sistem dasar, namun fungsi intinya tidak dapat dipanggil lagi sampai cadangan diisi ulang.

The end-of-year AMA provided a comprehensive look at 2025's progress and the roadmap ahead for 2026. The message was clear: building decentralized AI infrastructure for the long haul, not chasing market hype.
2025: Building before Scaling
Qubic hit serious technical milestones in 2025. The network now runs at a two-second tick speed with 2TB of memory support. This foundation is what enables Qubic to support large-scale computation, advanced AI models, and infrastructure-level applications. Custom mining integration with external projects like Monero proved that Qubic can work as a universal compute layer., advanced AI models, and infrastructure-level applications. Custom mining integration with external projects like Monero proved that Qubic can work as a universal compute layer.
On the developer side, multiple SDKs came online along with automated smart contract validation. The governance model matured too. Computors approved the first token halving, and voting mechanisms improved across the board.
Certik certified Qubic at 15.5 million TPS, positioning the network among the fastest blockchain infrastructures globally. This certification validates the technical foundation and opens doors for partnerships that require proven performance metrics.
The Madrid hackathon brought in 120 hackers across 27 teams, with €80,000 in prizes funded through partnerships with Telefonica and the Madrid government. This level of developer engagement doesn't happen by accident.
The momentum continued with RaiseHack 2025 in Paris, part of Europe's leading AI conference. The Qubic Track attracted 400 developers out of 6,000 total participants, with 22 teams advancing to finals at Le Carrousel du Louvre. Most recently, the "Hack the Future" hackathon drew 1,654 participants across 265 teams, resulting in 102 project submissions spanning smart contract development and no-code applications through EasyConnect integrations.
Beyond hackathons, the team made its presence felt at Token 2049 Singapore. With 25,000+ attendees, the event generated over 50 partnership leads and six active integrations. The workshop, upgraded to the main TON Stage, reaching hundreds of attendees. These efforts led directly to valuable collaborations, including Avicenne Studio, later winning the RFP for the Solana Bridge.
The Science Behind The Vision
David Vivancos and Dr. José Sanchez pushed forward on the AI research front. Two major papers were published: a theoretical AGI position paper that's been read over 16,000 times, and Neuraxon, a practical AI model already seeing traction with 2,500 reads and 129 code clones.
Unlike static language models, Neuraxon is designed as an evolving AI system rather than a fixed snapshot of intelligence. Integration into the Qubic network by spring 2026 will create what the team calls a "living AI system" that evolves over time. Traditional peer review processes slowed things down initially, leading to a pivot toward building practical models that demonstrate real progress.
Marketing That Moves Numbers
Since October, the marketing push has generated over 10 million ad impressions. CRM contacts jumped 730% from 536 to 4,451. Live stream AMAs crossed 100,000 views after switching to Streamyard for better distribution.
The paid analytics tell the story: 1300% performance increase on a modest $7,042 ad spend, with engagement metrics up 5656%. DeFiMomma, the marketing lead, emphasized building accountable systems before scaling further. No chaotic growth sprints, just measured execution.
For 2026, the positioning shifts and expands to establish Qubic as the most credible AI compute network for miners, computors, and developers. The brand identity will emphasize science, compute, and mathematical integrity. Global PR replaces short-term partnership hype. The target audience? Institutions that need to understand why decentralized AI infrastructure matters.
Ecosystem Reality Check
Alber, who has been leading ecosystem development, was refreshingly direct about what worked and what didn't. Some partnerships took longer than expected. Exchange integrations proved to be more complicated than anticipated, simply because Qubic's architecture differs from standard chains. External dependencies created delays.
The approach evolved to manage expectations better. A "fail fast, build fast" philosophy now guides incubation projects. Early MVP launches will replace long development cycles before community engagement. The focus areas for new projects are: interoperability bridges, stablecoins, and perpetual DEXs that leverage Qubic's speed advantage.
The Solana Bridge, being built by Avicenne studio after winning the RFP, should launch around May or June 2026. Alber confirmed he's stepping back from the public ecosystem lead role, though he'll continue supporting Qubic as a whole. The AI teams in the ecosystem are now self-sustaining.
What's Coming in 2026
The technical roadmap includes several key upgrades. Seamless updates will allow core network changes without downtime, which matters for partner exchanges. The mining algorithm continues evolving to support ongoing research. By year's end, the network transitions from AVX2 to AVX1212 instruction standards.
The Qubic Network Guardians program just launched to incentivize running light nodes through gamification and leaderboards. Making network participation accessible to more people strengthens decentralization.
Planning cycles shift to three-month time boxes with community-driven feature prioritization. The transparency should help ecosystem builders plan their own development timelines.
Community Culture Shift
2025 brought price volatility that tested the community. El Clip, the community workgroup lead, described a reshaping of identity. Moderation improved. The focus moved toward constructive criticism over reactive conflict.
The community is developing shared norms rather than top-down rules. Early intervention on disruptive behavior helps maintain productive discussions. Long-term contributors get recognition, which reduces friction.
The expectation for 2026? Consolidate this new identity. Open participation continues, but the culture rewards substance over speculation. Short-term thinking gets left behind.
The Core Philosophy
Throughout the AMA, one theme kept surfacing: Qubic exists to build decentralized AI infrastructure that solves complex problems. The token facilitates the economy, but the real value lives in the technology itself.
Alber framed it directly:
Even without the token, Qubic enables powerful outsourced computation and AI development. That's the foundation everything else builds on.
The reality is that AI advances so rapidly that rigid plans become obsolete. Flexibility matters. Iterative development matters. Continuous adaptation to new research matters.
The next three to five years aim to create an AI economy with interconnected agents and high-speed crypto applications. Qubic positions itself as the compute network where users actually want to deploy their workloads.
Looking Forward
The AMA focused on substance over speculation. The team laid out technical milestones, acknowledged where mistakes were made, and outlined concrete plans for 2026.
The scientific research continues pushing boundaries. The developer ecosystem keeps growing. The marketing strategy targets credibility and consistency over short term hype. The community matures into something sustainable.
The goal is clear: become the most powerful decentralized AI compute network. The 2025 foundation is solid, and the 2026 roadmap focuses on execution and advancements.
The pieces are moving into place. Now, comes the hard part: delivering on all of it.
🌐 https://qubic.org #Qubic

Analisis Pemberian informasi pada jaringan saraf dalam dengan prinsip multiskala
Di otak, neuromodulasi adalah sekumpulan mekanisme melalui mana neurotransmitter tertentu memodifikasi properti fungsional neuron dan sinapsis, mengubah bagaimana mereka merespons, berapa lama mereka mengintegrasikan informasi, dan dalam kondisi apa mereka berubah dengan pengalaman.
Efek ini dihasilkan terutama melalui neurotransmitter seperti dopamin, serotonin, noradrenalin, dan asetilkolin, yang bertindak pada reseptor yang dikenal sebagai reseptor metabotropik. Tidak seperti reseptor cepat, ini tidak langsung menghasilkan sinyal listrik, tetapi sebaliknya mengaktifkan jalur sinyal seluler yang memodifikasi rezim dinamis neuron dan sirkuit.

Ditulis oleh Tim Ilmiah Qubic

Bagaimana cara kerja neuron seiring waktu?
Neuron biologis tidak berfungsi seperti saklar lampu kamar tidur yang dinyalakan. Mereka adalah sistem dinamis yang terus-menerus. Status neuronal berkembang secara konstan, bahkan tanpa rangsangan eksternal.
Bagaimana cara kerja neuron seiring waktu?
Pada dasarnya, dengan memindahkan muatan listrik (ion) masuk atau keluar dari membrannya, yaitu, dengan mengubah potensial listriknya. Ion masuk atau keluar (terutama natrium dan kalium) melalui berbagai gerbang neuron dengan intensitas tertentu, memodifikasi potensial. Ada beberapa gerbang, yang disebut gerbang kebocoran, di mana ion selalu masuk dan keluar.

Ditulis oleh Tim Ilmiah Qubic
Akademi Kecerdasan Neuraxon — Volume 3

1. Neuromodulasi di Otak: Dasar dari Kecerdasan Adaptif
Neuromodulasi mengacu pada seperangkat mekanisme yang mengatur bagaimana sistem saraf berfungsi pada momen tertentu, tanpa mengubah arsitektur dasarnya. Berkat neuromodulasi, otak dapat belajar dengan cepat atau lambat, menjadi eksploratif atau konservatif, dan tetap terbuka terhadap hal baru atau fokus pada apa yang sudah dikenal. Pengkabelan tidak berubah; yang berubah adalah cara pengkabelan itu digunakan. Konsep ini sangat penting untuk memahami AI yang terinspirasi otak dan arsitektur di balik Neuraxon Qubic.

Written by Qubic Scientific Team

The brain is dynamic and non-binary
Biological brain networks do not operate as a decision switch between activation and rest. In living systems, inactivity itself implies dynamism. Absolute “rest” would be incompatible with life. As we saw in the first chapter, life unfolds in time.
An individual neuron may appear as an all-or-nothing event, transmitting electrical current to another neuron in order to inhibit or excite it. However, prior to that transmission, the action potential, the neuron continuously receives positive and negative inputs in a region called the dendrites. If the global sum of these inputs exceeds a certain threshold, a physical conformational change occurs, and the electrical current propagates along the axon toward the next neuron. For most of the time, neuronal processing takes place below the action threshold, where excitatory and inhibitory currents are continuously integrated. 
In computational neuroscience, it is well established that the brain is a continuous dynamic system whose states evolve even in the absence of external stimuli (Deco et al., 2009; Northoff, 2018).
There are no discrete events or resets in the brain. Each external stimulus acts upon a living system that already has a prior configuration. A stimulus may bias an excitatory or inhibitory state, but never a static one. It is like a ball on a football field: the same trajectory triggers different outcomes depending on the dynamic positions of the players. With an identical path, the play may fail or become a decisive assist.
The mechanisms that keep neurons active independently of immediate stimuli are well known.
One of them consists of subthreshold inputs, which alter the membrane potential without generating an action potential. 
Others include silent synapses and dendritic spines, which preserve latent connectivity between neurons or promote local activation. 
The most important mechanism involves metabotropic receptors linked to neurotransmitters, which organize context. They don't directly determine whether an action potential is triggered. Instead, they define what is relevant or not, what reward prediction a stimulus carries, what level of alert or danger is present, how much novelty exists in the system, what degree of sustained attention is required, what balance between exploration and exploitation is appropriate, what should be encoded versus forgotten, how the internal state is regulated, and when impulse control or temporal stability is advantageous.
In other words, metabotropic receptors implement a form of wise metacontrol. They are not data, but parameters! They function as dynamic variables that adjust system behavior. They allow the system to become sensitive to the functional meaning of a situation (novelty, relevance, reward, or threat) without requiring immediate responses. 
Returning to the football metaphor, metabotropic receptors correspond to team tactics: deciding when to attack or defend, that is, deciding how the game is played.
From a computational perspective, these mechanisms operate through intermediate states. They are not binary (active/inactive). The system operates in three modes: excitatory, inhibitory, and an intermediate state that produces no immediate output but modulates future dynamics.
When we speak of ternary in biological brain networks, we are not referring to a mathematical abstraction or calculus but to a literal functional description of how the brain maintains balance over time.
For this reason, computational neuroscience does not primarily study input–output mappings, but rather how states reorganize continuously. These states are fundamentally predictive in nature (Friston, 2010; Deco et al., 2009).
LLMs are binary computations.
In large language models, the concept of ternarity does not make sense. Learning is fundamentally based on error backpropagation. That is, once the magnitude of the error relative to the expected data is known, an optimization algorithm adjusts parameters using an external signal.
How does this work? The model produces an output, for example the prediction of the most likely next word: “Paris is the capital of …”. If the response is Finland, this is compared with the correct word from the training set (France). From this comparison, a numerical error is computed. This error quantifies how far the prediction deviates from the expected value. The error is then transformed into a gradient, namely a mathematical signal that indicates in which direction and by how much the model’s parameters should be adjusted to reduce the error. The weights are updated backward only after the output has been produced and evaluated.
The error is computed a posteriori, the weights are adjusted so that the correct response becomes France, and the system resumes operation as if nothing had happened.
In large language models, the separation between dynamics and learning is especially pronounced. During inference, parameters remain fixed; there is no online plasticity, no habituation, no fatigue, and no time-dependent adaptation. The system does not change by being active.
In the football metaphor, LLMs resemble a coach who reviews mistakes after the match and adjusts tactics for the next one. But during the match itself, the team plays the full ninety minutes without any possibility of technical or tactical modification! 
There is pre-match strategy and post-match correction, but no dynamism during play! 
LLMs are therefore not ternary in a functional sense. They are matrices of “attention” (transformers) trained offline (Vaswani et al., 2017). This is not a quantitative limitation but an ontological difference.
Neuraxon and Aigarth trinary dynamics
Neuraxon introduces a fundamentally different framework. Its basic unit is not an input–output function, as in LLMs, but an internal continuous state that evolves over time. In Neuraxon, excitation is represented as +1, inhibition as −1, and between these two states there exists a neutral range represented by 0.
At each moment, the system integrates the influence of current inputs, recent history, and internal mechanisms in order to generate a discrete trinomial output (excitation, inhibition, or neutrality).
The relationship between time and ternary is central. The neutral state does not represent the absence of computation or inactivity but a subthreshold phase in which the system accumulates influence without producing immediate output. It is comparable to a dynamic tactical shift in a football team, regardless of whether it leads to a goal for or against.
Aigarth expresses the same logic at a structural level. Not only are the units themselves ternary, but the network can grow, reorganize, or collapse depending on its utility, introducing an evolutionary dimension that reinforces continuous adaptation. The Neuraxon–Aigarth combination (micro–macro) gives rise to computational tissues capable of remaining active (intelligence tissue units), something impossible for architectures based exclusively on backpropagation.

The hardware question cannot be ignored. At present, there is no general-purpose ternary hardware, but there are active research lines in ternary logic, including multivalued memristors and neuromorphic computation based on resistive or spintronic devices (Yang et al., 2013; Indiveri & Liu, 2015). These approaches aim to reduce energy consumption and, more importantly, to achieve ternary computation aligned with physical, living, and continuous dynamics.
Does a ternary architecture make sense even without dedicated ternary hardware? Despite this limitation, it does, because architecture precedes physical substrate. By designing ternary systems, we reveal the inability of binary logic to reflect a dynamic world. At the same time, ternary architectures such as Neuraxon–Aigarth can already yield improvements on existing binary hardware by reducing unnecessary activity.
References
Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. J. (2009). The dynamic brain: From spiking neurons to neural masses and cortical fields. PLoS Computational Biology, 5(8), e1000092.
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.
Indiveri, G., & Liu, S.-C. (2015). Memory and information processing in neuromorphic systems. Proceedings of the IEEE, 103(8), 1379–1397.
Northoff, G. (2018). The spontaneous brain: From the mind–body problem to a neurophenomenology. MIT Press.
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013). Memristive devices for computing. Nature Nanotechnology, 8(1), 13–24.
#aigarth #trinary

Written by $Qubic Scientific Team

Neuraxon Intelligence Academy — Volume 4

The word network shows up constantly in both neuroscience and artificial intelligence. But despite sharing the same label, biological neural networks and artificial neural networks are fundamentally different systems. To understand what each one actually does, and where a third approach fits in, we need to look at the architecture and behavior of networks at every level.
Biological Neural Networks: How the Brain Processes Information
A biological neural network is a system of interconnected neurons whose function is to process information and generate behavior. These networks are dynamic. They stay active over time, even when we are not consciously engaged in any task. They carry an energetic cost, which in the case of the human brain is remarkably low for the complexity it produces.
Biological networks integrate both internal and external signals using their own language: time-frequency. Think of a musical band with multiple instruments playing at different rhythms. The bass drum carries the tempo, the bass plays two notes per beat, and the cymbals fill in the sixteenth notes. The melody moves freely without losing the beat. The musicians couple their scores at different rhythms that fit together perfectly. These are nested frequencies, and this is exactly how brain networks function. The time-frequency language of different networks nests within itself, a concept known as cross-frequency coupling.
From Single Neurons to Massive Networks
Everything begins with the neuron. That single nerve cell generates an action potential, a brief electrical impulse that propagates along the axon. The neuron receives signals through the dendrites, integrates them in the soma, and transmits the signal if it surpasses a threshold. We covered this process in detail in NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time and NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence.
Neurons connect to other neurons through chemical synapses, where neurotransmitters are released (see NIA Volume 3: Neuromodulation and Brain-Inspired AI), or through electrical synapses, where current passes directly between cells. To form networks, many neurons interconnect and create recurrent circuits. But this integration is non-linear, meaning the response of the whole does not equal the simple sum of its parts. The magnitude is staggering: the human brain contains approximately 86 billion neurons and somewhere between 10¹⁴ and 10¹⁵ synapses (Azevedo et al., 2009).
Small-World Properties and Excitation-Inhibition Balance
At the topological level, these networks display small-world properties: high local clustering combined with short global connections. This architecture enables efficient communication across the brain while maintaining specialized local processing.
The functioning of biological neural networks depends on the balance between excitation and inhibition. If excitation dominates, activity destabilizes. If inhibition dominates, the network goes silent. Dynamic stability arises from the balance between both forces. This balance is maintained through synaptic plasticity, the mechanism that allows the strength of connections to change based on experience. On top of that, neuromodulation adjusts circuit gain, controlling how strongly an input produces an output (Marder, 2012). In a threatening situation, for example, noradrenaline increases sensory sensitivity and the capacity for rapid learning.
Multiple Temporal Scales and Cerebral Cortex Brain Function
Networks operate at multiple temporal scales simultaneously. At the neuronal level, action potentials fire in milliseconds. Neuronal oscillations unfold in seconds. Synaptic changes develop over hours or days, and structural reorganization happens across years. Everything works in a harmonic, dynamic, and intertwined pattern.
But not everything communicates with everything without structure. The cerebral cortex brain function is organized into specialized networks. The most important include the default mode network, linked to self-reference and thinking about the self and others; the central executive network, linked to direct task execution; the salience network, which detects what is relevant at each moment and allows switching between different modes; the sensorimotor network that sustains voluntary movements; and various attention networks. Humans also possess a distinctive language network, enabling both comprehension and production of language.
In biological networks, no isolated note is a symphony. The symphony emerges from the dynamic pattern of relationships between notes. The brain does not contain things. It does not store memories the way a hard drive stores files. The brain constructs dynamic configurations.
Courtesy from DOI: 10.3389/fnagi.2023.1204134
Artificial Neural Networks: How Deep Learning Models Work
An artificial neural network (ANN) is a mathematical model designed to approximate complex functions from data. It draws abstract inspiration from the brain: it uses interconnected units called "artificial neurons," but these are not cells. They are algebraic operations. Calling an algebraic operation a neuron is arguably an exaggerated extrapolation, and calling language prediction "intelligence" may be equally misleading. But since these are the established terms, it is important to understand them and separate substance from hype.
How an Artificial Neuron Works
Each artificial neuron performs three steps. First, it receives a set of numerical inputs. Then it multiplies each input by a synaptic weight, which is an adjustable parameter. Finally, it sums the results and applies an activation function that introduces non-linearity. Common activation functions include the Sigmoid, which compresses values between 0 and 1, and ReLU (Rectified Linear Unit), which cancels negative values and lets positive ones pass through.
Without non-linearity, the network would simply perform a linear transformation, incapable of modeling complex patterns. ANNs are organized into input layers, where data enter; hidden layers, where data are progressively transformed; and an output layer, which generates the prediction.

From the Perceptron to Deep Learning
All modern architectures trace their origins to the perceptron (Rosenblatt, 1958), a simple linear neuron with a threshold. Modern deep learning networks can contain hundreds of layers and billions of parameters. But at their core, an ANN functions like an enormous automated spreadsheet that adjusts millions of numerical cells until the output matches the expected result.
Backpropagation and Gradient Descent: How Artificial Networks Learn
Learning in artificial networks does not work the way biological learning does. There is no adjustment of neuromodulators or synaptic intensity based on lived experience. Instead, learning is based on minimizing an error function that quantifies the difference between the network's prediction and the correct answer.
Consider a simple example: the model is asked to complete "Paris is the capital of..." If the prediction is Italy, the error function measures the gap between Italy and France, then adjusts the weights accordingly. The central mechanism behind this adjustment is backpropagation (Rumelhart et al., 1986). This algorithm calculates the error at the output, propagates that error backward layer by layer, and adjusts the weights using gradient descent, a mathematical method that modifies parameters in the direction that reduces the error.
Formally, learning consists of optimizing a differentiable function in a space of many dimensions. If you think of physical space, the dimensions are x, y, and z. But in language, imagine dimensions like singular, plural, feminine, masculine, verb, subject, attribute, noun, adjective, intonation, and synonym. Introduce millions of dimensions and enough computational power, and a model can learn that Paris is the capital of France simply by reducing prediction errors during training.
Architectures of Artificial Neural Networks
Although the terminology overlaps with neuroscience, the process does not resemble how a living system learns. In an ANN, adjustment depends on global calculation and explicit knowledge of the final error. The network needs to know exactly how wrong it was.
If a network learns to recognize cats, it receives thousands or millions of labeled images. Each time it fails, it slightly adjusts the weights. After millions of iterations, the internal pattern stabilizes into a configuration that discriminates cats from other objects. The process is purely statistical. The network does not "understand" what a cat is. It detects numerical correlations in pixels. It does not hold a "world model" of a cat, only matrices of numbers on massive scales. For a deeper look at why this matters, read our analysis of benchmarking world model learning.
There are several key architectures of artificial neural networks. Convolutional networks (CNNs) use spatial filters that detect edges, textures, and hierarchical patterns, making them essential for computer vision. Recurrent networks (RNNs, LSTMs) incorporate temporal memory for processing sequences. And the now-dominant Transformers use attention mechanisms that dynamically weight which parts of the input are most relevant (Vaswani et al., 2017). Transformers currently power most large language models in natural language processing.
The growth of these networks does not happen organically as in living systems. It happens through explicit design and parameter scaling via massive training in high-performance computing centers. Adaptation is limited to the training period. Once trained, the network does not spontaneously reorganize its architecture. Any modification requires a new optimization process. As we explored in That Static AI Is a Dead End, this frozen nature is a fundamental limitation of current AI systems.
Despite sharing the name "network," the similarity between artificial and biological neural networks is limited. The analogy is structural and abstract: both use interconnected units and learning through adjustment of connections. But the brain is an evolutionary, embodied, and self-regulated system. An ANN is a function optimizer in a numerical space.
Between Biological and Artificial Networks: How Neuraxon Aigarth Bridges the Gap
The networks simulated in Neuraxon Aigarth are conceptually positioned between biological networks and conventional artificial neural networks. They are not living tissue, but they are not merely mathematical functions optimized by gradient either. Their objective is to approximate dynamics typical of biological systems, including multiscale plasticity, context-dependent modulation, and self-organization, all within a computational framework built for Qubic's decentralized AI infrastructure.
If in Volume 1 we described self-organized metabolic systems and in Volume 2 we explored differentiable optimizing functions, Neuraxon attempts to incorporate dynamic properties of the former without abandoning the mathematical formalization of the latter.
Trivalent States: Capturing Excitation-Inhibition Balance
Instead of typical continuous activations (real values after a ReLU, for example), Neuraxon uses trivalent states: -1, 0, and +1. Here, +1 represents excitatory activation, -1 represents inhibitory activation, and 0 represents rest or inactivity.
This scheme does not attempt to copy the biological action potential. Rather, it captures the functional principle of excitation-inhibition balance described in the biological networks section above. In the brain, stability emerges from the balance between these forces. In Neuraxon, the discrete state space imposes a dynamic closer to state-transition systems than to simple continuous transformations.
In contrast to classical artificial networks, where activation is a floating-point number without physiological meaning, the trivalent system imposes structural constraints that shape how activity propagates through the network.
Dual-Weight Plasticity: Fast and Slow Learning
Biological neural networks exhibit plasticity at different temporal scales: rapid changes in synaptic efficacy and slower consolidation over time. Neuraxon introduces this idea through two weight components:
w_fast: rapid changes that are sensitive to the immediate environment.
w_slow: slow changes that stabilize repeated patterns over time.
This prevents the system from depending exclusively on a homogeneous weight update like standard backpropagation. Part of learning can be transient, while another part is gradually consolidated. This mechanism introduces a dimension absent in most artificial neural networks: the learning rate is not fixed, but dependent on the global state of the system.
Contextual Neuromodulation Through the Meta Variable
In biological networks, neuromodulators such as noradrenaline and dopamine do not transmit specific informational content. Instead, they alter the gain and plasticity of broad neuronal populations. We explored this in depth in NIA Volume 3: Neuromodulation and Brain-Inspired AI.
In Neuraxon, the variable meta plays a functionally analogous role. It does not encode specific information, but modifies the magnitude of synaptic updating. This approximates the biological principle that learning depends on motivational or salience context. In a conventional artificial network, the gradient is applied uniformly based on error. In Neuraxon, learning can be intensified or attenuated according to internal state or global external signals.
The conceptual difference is significant. In classical deep learning networks, error drives learning. In Neuraxon, error can coexist with a contextual modulatory signal that alters how much is learned at any given moment.
Self-Organized Criticality and Adaptive Behavior
Biological networks operate near a regime called self-organized criticality, where the system maintains equilibrium between order and chaos. This regime allows flexibility without loss of stability.
Neuraxon models this property by allowing the network to evolve toward intermediate dynamic states in which small perturbations can produce broad reorganizations without collapsing the system.
In models such as the Game of Life extended with proprioception that the team is currently developing, the system can receive external signals (environment) and internal signals (its own state, energy, previous collisions). If an agent repeatedly collides with an obstacle, an increase in the meta signal may be generated, analogous to an increase in arousal. That signal temporarily increases plasticity, facilitating structural reorganization.
Here, the network does not learn only because it makes mistakes. It learns because the environment acquires adaptive relevance. The similarity with the brain remains limited: Neuraxon does not possess biology, metabolism, or subjective experience. However, it introduces dynamic dimensions absent in most conventional artificial neural networks, positioning it as a genuinely novel approach to brain-inspired AI on decentralized infrastructure.
The computational power required to run Neuraxon simulations is provided by Qubic's global network of miners through Useful Proof of Work, turning AI training into the consensus mechanism itself.

Scientific References
#Azevedo, F. A. C., et al. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology, 513(5), 532-541. DOI: 10.1002/cne.21974
#Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76(1), 1-11. DOI: 10.1016/j.neuron.2012.09.010
#Rosenblatt, F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. DOI: 10.1037/h0042519
#Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. DOI: 10.1038/323533a0
#Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. arXiv: 1706.03762
Brain network images courtesy from: DOI: 10.3389/fnagi.2023.1204134
#Aİ #AGI