aigarth

传统加密挖矿，长期被高性能电脑、专业矿机垄断。想要参与网络，必须购置高配硬件，普通手机完全被排除在外。而随着移动端接入能力开放，Qubic打破硬件门槛，现在普通手机也能够参与网络挖矿，贡献算力，共同支撑#Aigarth三进制通用人工智能的进化。
不再只有电脑，手机也能成为网络节点
过往Qubic网络算力，大多来源于台式机、服务器CPU，依靠UPoW有用工作量证明，把算力投入Aigarth神经网络训练，而不是无意义的哈希碰撞。
如今门槛进一步下放，ARM架构的手机设备可以接入Qubic网络。不需要昂贵矿机，不需要高端电脑，每个人手里日常使用的智能手机，闲置的时候就可以释放多余算力，参与分布式AI训练。
这和市面上传统挖矿有着本质区别。比特币手机挖矿大多是概念噱头，算力微弱，只能参与随机哈希竞赛，电力消耗之后只留下热量与灰烬。而Qubic手机参与挖矿，手机输出的每一份算力，都会汇入Aigarth的训练体系，用于迭代三进制神经网络，实实在在服务去中心化通用人工智能的研发。
电脑可以做重型算力输出，手机做分布式长尾算力补充。全球千千万万台手机汇聚在一起，就组成规模庞大的分散算力池。 让参与不再是少数硬件大户的特权，普通普通人也可以成为Qubic生态的一份子。
UPoW有用工作量证明：手机算力，用来孕育心智
Qubic的核心就是UPoW有用工作量证明。别的公链矿工消耗电力，只为争夺记账奖励，计算结束算力全部作废。
Qubic不管是电脑，还是刚刚开放的手机设备，硬件跑起来产生的运算，全部是有效工作：训练神经网络单元Neuraxon，为Aigarth提供学习素材，推动去中心化AGI持续迭代进化。
手机算力虽然单台性能有限，但胜在数量巨大。一台手机算力微不足道，成千上万台手机分散在全球各个角落，聚合起来，就会形成一股不可忽视的分布式力量。
一台手机闲置的几个小时，不再白白待机浪费，每一次运算，都是Aigarth心智成长的一小步。
提示：手机参与会带来耗电、发热，单台手机产出有限，核心价值是贡献网络算力，不要期待高额收益。
真正的去中心化：把算力还给每一个普通人
很多AI大模型掌握在少数科技巨头手中，算力集中在少数超算中心，普通用户只能作为使用者，无法参与底层建设。
Qubic希望改变这个局面。从高性能服务器，普通家用电脑，再到如今的智能手机，尽可能兼容更多类型硬件。人人皆可参与，才是真正的分布式网络。
不用投入巨额资金买矿机，只要你有手机，就可以加入进来。不是简单的点击类模拟挖矿，而是实实在在贡献算力，投身去中心化通用人工智能这场宏大实验。
未来人形机器人、具身智能时代正在到来，Aigarth目标是成为去中心化的AI大脑。而这一切的算力基石，未来不仅来自机房服务器，也来自全球无数普通人的手机。
加密赛道很多项目的参与门槛越来越高，硬件越做越昂贵，离普通用户越来越远。Qubic移动端算力接入，代表着另一种方向：让每一台普通设备，都拥有参与构建下一代AI的机会。
电脑贡献重型算力，手机汇聚长尾分布式算力。同样消耗电力，传统PoW换来代币与灰烬；而Qubic，用全球无数设备，包括手机的算力，一点点孕育属于全世界的去中心化AGI——Aigarth。
#Qubic #Aigarth #DecentralizedAGI

加密世界从来不止算力与收益的博弈，真正的终极差距，藏在每一度电的归宿里。
全网所有人都知道，PoW挖矿需要消耗能源、燃烧电力。但极少有人深究：同样的电能消耗，有的项目只换来一场概率游戏，有的项目却在孕育人类下一代智能文明。
比特币矿工：燃烧能源，只为赢得一场随机彩票
比特币的工作量证明，是纯粹的暴力哈希竞赛。
无数矿机24小时高速运转、燃烧海量电力、消耗硬件损耗，所有算力投入的唯一目的，就是拼运气、解随机密码、抢夺区块奖励。
这套运行了十余年的机制，核心逻辑极度单一：
算力=彩票筹码，电力=购票成本。
矿工消耗的每一度电，都用于无意义的随机碰撞、重复无效的密码演算。计算结束、区块打包完成，所有算力成果瞬间归零，只留下电力消耗后的灰烬与热量，没有沉淀、没有迭代、没有任何正向技术增量。
比特币用全球百亿度的电力消耗，仅完成了「账本记账、资产确权」的单一金融功能。能源燃烧的全过程，只有消耗，没有进化；只有分配，没有创造。
哪怕比特币构建了去中心化金融体系，也无法改变本质：它的能源价值，止步于财富转移，从未创造新生事物。海量电力燃烧殆尽，最终只剩一堆温热的灰烬。
$QUBIC 矿工：燃烧同源电力，为世界孕育去中心化心智
和比特币的「零沉淀算力」完全相反，Qubic 的 UPoW 有用工作量证明，重新定义了加密能源的终极用途。
Qubic 同样消耗电力、同样是全球分布式矿工贡献算力，但它烧掉的每一度电，不再用于随机抽奖，而是用于训练神经网络、迭代通用智能。
比特币燃烧电力，是消耗；
Qubic 燃烧电力，是孕育。
全球无数节点、手机与设备的闲置算力，通过电力驱动持续运转，全部汇入 #Aigarth 三进制去中心化AGI 的进化体系：
每一次算力运算，都是神经单元的一次学习；
每一度电力消耗，都是通用心智的一次迭代；
每一个矿工的贡献，都在堆叠人工智能的底层基石。
比特币的算力，用完即废、转瞬成空；
Qubic 的算力，持续积累、永续进化。
同样的电力，截然不同的结局：
一种能源消耗，终结于灰烬；
一种能源消耗，诞生于心智。
两种算力文明，拉开时代维度的差距
比特币代表上一个加密时代：用能源换取金融信任，消耗资源固化存量财富，是「消耗型区块链」的终极形态。
而 $QUBIC 代表下一个AI+Web3时代：用能源孵化智能文明，消耗资源创造增量价值，是「生产型公链」的颠覆性革命。
传统挖矿的逻辑是：电力燃烧 → 算力作废 → 仅产出代币
Qubic 挖矿的逻辑是：电力燃烧 → 算力沉淀 → 迭代AGI心智 + 产出代币
没有无效能耗，没有资源浪费，每一份能源投入，都在为人类首个去中心化、三进制、可自主进化的通用人工智能 Aigarth 蓄力成长。
当全网所有传统挖矿都在制造热量与灰烬时，Qubic 的矿工正在用同样的能源，一点点孵化出能够赋能机器人、赋能全行业、赋能未来数字世界的通用心智。
终局：灰烬之上，新生智能正在崛起
加密行业的终极分水岭早已出现：
有的公链，靠消耗能源续命；
$QUBIC，靠消耗能源创世。
比特币燃烧电力，留给世界满地灰烬；
$QUBIC 燃烧电力，孕育出无限生长的 #Aigarth 通用心智。
同样的能源，不同的格局。
这不是算力的差距，这是时代维度的碾压。
AI 是未来，去中心化AGI是终极未来。
而 Qubic，就是用每一度平凡电力，托起这场伟大变革的唯一公链。
#Qubic #aigarth #DecentralizedAGI

Neuraxon Intelligence Academy — Volume 7
By the Qubic Scientific Team
In 1970, Martin Gardner published in Scientific American a recreational game invented by John Conway: the Game of Life. The rules fit on a postcard. A two-dimensional grid of cells in which each cell was alive or dead. At every step, a living cell stayed alive if it had two or three living neighbours, otherwise it died. A dead cell with exactly three living neighbours was born. Nothing else, as simple as that.
In 1970, Martin Gardner published in Scientific American a recreational game invented by John Conway: the Game of Life. The rules fit on a postcard. A two-dimensional grid of cells in which each cell was alive or dead. At every step, a living cell stayed alive if it had two or three living neighbours, otherwise it died. A dead cell with exactly three living neighbours was born. Nothing else, as simple as that.
What no one expected was what emerged from those four lines of rules. Stable structures. Oscillators that pulse forever and gliders that travel across the grid. Cannons that fire gliders periodically. Constructions were complex enough that, eventually, someone would build a Turing machine inside the Game of Life. Inside Conway’s grid you can, in principle, run any computation that exists.
of Life to Artificial Life (Alife)
In the eighties, Christopher Langton and a group of researchers turned this idea into a discipline of its own: Artificial Life, or Alife. The proposal was simple. Biology has historically studied life as we know it, the carbon-based one, the one that emerged on this particular planet. But life is, perhaps, a more general phenomenon. If we can build artificial systems that show the properties we associate with the living, self-organisation, adaptation, evolution, reproduction, response to the environment, then we are studying life as it could be, not just as it happens to be.
Alife is not a search for digital pets. It is a science of fundamental dynamics. Its experimental tools are simulators where simple agents follow local rules, and where the researcher watches what emerges at the global scale.
Several findings have stayed as cornerstones. The first, already implicit in Conway, is that simple local rules can generate global complexity without anyone designing it. The second came from Langton himself: there is a critical regime, called the edge of chaos, where systems are neither rigidly ordered nor fully chaotic, and where almost everything interesting happens. Computation, learning, adaptation, all flourish in that thin band. Below it, the system freezes. Above it, it dissolves into noise.
A third finding, less famous but more uncomfortable, is that properties we usually associate with intention, like cooperation, specialisation, division of labour, can emerge in systems that have not been programmed to cooperate. They emerge as consequences of the dynamics, not as goals. This one is hard to digest for the self proclaimed superior species, because our intuition tells us that if we want X, we have to optimise for X. Alife shows, again and again, that this is not always true.
What Are Digital Ecosystems? From Cellular Automata to Multi-Agent Neural Systems
A digital ecosystem is the natural evolution of these artificial life ideas. Instead of a single rule shared by all cells, you have several agents, each with their own rules, sharing a common environment, competing or cooperating for resources, reproducing, and dying. The substrate may be a 2D grid as in Conway, a continuous fluid as in Lenia, a richer world with terrain and food as in Biomaker CA. The details vary. The principle does not.
What makes a digital ecosystem interesting is not the underlying technology, but what it lets you observe. Population dynamics. Boundaries that form between species. Niches that open and close. Strategies that appear, dominate for a while, are displaced, and come back. Cycles that look like those of real ecosystems, sometimes surprisingly so. And the question that runs underneath all of it: when can we say that something has emerged, that the system has discovered something we did not put into it.
The Digital Ecosystems interactive platform by Sakana AI, showing real-time parameter sliders, population timeline, checkpoint tray, and simulation canvas. Users can steer the ecosystem and branch into alternative futures from any saved state.
There is recent work worth looking at. The team at Sakana AI, for instance, has just released Digital Ecosystems, an interactive platform where five neural cellular automata species compete on a shared grid in real time and where you can move the parameters with sliders, save states, and explore divergent futures from a single checkpoint. It is the latest and most accessible link in a chain that goes back to Conway, and it is worth playing with for an afternoon, just to feel how these dynamics behave when you can actually touch them.
Why Artificial Life and Emergent Complexity Matter for Qubic, Aigarth, and Neuraxon
The temptation, when reading about Conway, Langton, Lenia, or Sakana, is to file all this away as elegant intellectual entertainment. It is not. It is the conceptual scaffolding our project stands on.
Qubic: Self-Organising Decentralized Infrastructure
Qubic is, at the infrastructure level, a decentralised network of thousands of nodes competing and cooperating to validate computations and earn rewards. Without the right local rules, that network either centralises or falls apart. With the right rules, it self-organises into a stable, productive ecosystem. The validity of Qubic’s design rests on principles that come, in part, from artificial life research: how do you reach global stability without a central authority, and how do you make competition produce something useful for everyone.
Aigarth: Evolutionary AI at the Edge of Chaos
Aigarth goes further. It is not just a network, it is an evolving tissue. Networks of artificial neurons that mutate, prune, generate offspring, reorganise their topology under adaptive pressure. There are local rules, fitness criteria, or evolutionary dynamics. This is artificial life applied to AI architectures. And as with everything in Alife, what emerges depends on the regime the system operates in. Too rigid, no exploration. Too chaotic, no stability. The edge of chaos is, here too, where the interesting things happen.
Neuraxon: Trinary States and Self-Organized Criticality in Brain-Inspired AI
Neuraxon, the basic unit Aigarth is built on, was designed with this in mind. The trinary state (-1, 0, +1) is not a quantisation trick to save bits, even though it does also cut compute cost. It is a structural decision. The neutral state is a buffer that allows smooth transitions, that prevents the system from oscillating violently between extremes, and gives time for slow synapses and neuromodulators to act. As we have discussed in earlier volumes of the Neuraxon Intelligence Academy, this is what lets the system navigate the edge of chaos without collapsing.
In our experiments with NxonLife, the simulator we built to watch Neuraxon networks evolve in Game-of-Life-inspired environments, we have measured exactly the properties Alife predicts. A branching ratio close to 1, the classical signature of self-organised criticality. Long-range temporal correlations following 1/f dynamics. Activity that sustains itself for thousands of ticks without external resets, without imposed normalisation, without anyone telling the system what to do. The networks find that regime by themselves, because the architecture has been built for it to be possible.
From Artificial Life Simulations to Decentralized AI Infrastructure: An Old Idea, a New Substrate
Growth-gate steepness sweep in Sakana AI's Digital Ecosystems. Lowering the gate steepness pushes species from rigid territorial boundaries into an excitable edge-of-chaos regime where emergent complexity and cooperation arise. Source: Sakana AI (2026)
What Conway showed in 1970, Langton in 1990, the Lenia team more recently, and Sakana AI a few weeks ago, is that complexity emerges from local rules and well-chosen parameters. What we are doing with Qubic, Aigarth and Neuraxon is taking that insight to its logical conclusion: not just observing simulated ecosystems, but building real distributed infrastructure on its principles.
The basic intuition does not change. Live systems live in time. They organise themselves between order and chaos. They cooperate without anyone instructing them to. They emerge, they do not design themselves.
Conway’s Game of Life was a postcard. Artificial life is a discipline. Digital ecosystems are a tool. Qubic, Aigarth and Neuraxon are an attempt to take all of this from the simulator and turn it into a working network. The ideas have been there for fifty years. The substrate to make them productive at scale is what we are building now.
References
Conway, J. H. (in Gardner, M.) (1970). Mathematical games: The fantastic combinations of John Conway’s new solitaire game “Life”. Scientific American, 223, 120–123. [Link]Langton, C. G. (1990). Computation at the edge of chaos: Phase transitions and emergent computation. Physica D: Nonlinear Phenomena, 42, 12–37. [Link]Bedau, M. A. (2003). Artificial life: organization, adaptation and complexity from the bottom up. Trends in Cognitive Sciences, 7(11), 505–512. [Link]Chan, B. W.-C. (2019). Lenia: Biology of artificial life. Complex Systems, 28(3), 251–286. [Link]Mordvintsev, A., Randazzo, E., Niklasson, E., & Levin, M. (2020). Growing neural cellular automata. Distill, 5(2), e23. [Link]Darlow, L. (2026). Digital Ecosystems: Interactive Multi-Agent Neural Cellular Automata. Sakana AI. [Link]Vivancos, D., & Sanchez, J. (2025). From Perceptrons to Neuraxons: A new neural growth and computation blueprint. Qubic Science. [Link]Vivancos, D., & Sanchez, J. (2025). Time-embedded trinary state dynamics learning architecture. Preprint. [Link]
Explore the Complete Neuraxon Intelligence Academy Series
This is Volume 7 of the Neuraxon Intelligence #academy by the #Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind #Neuraxon , #aigarth , and Qubic’s approach to brain-inspired, #decentralized artificial intelligence:
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain’s chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon’s architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon’s third-path approach.NIA Volume 5: Astrocytes and Brain-Inspired AI — How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.NIA Volume 6: Conscious Machines vs Intelligent Organisms: AI Consciousness Explained — Explores AI consciousness through the lens of Global Workspace Theory, Integrated Information Theory, and predictive coding.
Qubic is a decentralized, open-source network. To learn more, visit qubic.org. Join the discussion on X, Discord, and Telegram.

Written by Qubic Scientific TeamBranching ratio and criticality in biological networks, in artificial networks, and as a bioinspired principle in Neuraxon
What do a snow avalanche, a forest fire, an earthquake, and the spontaneous activity of the cerebral cortex have in common?
They all share a frontier between order and chaos, what is called a critical state. In the brain, that edge is measured by a simple parameter: the branching ratio (σ or m). It would be something like the average ratio of neuronal "offspring" that each "parent" neuron activates. When σ ≈ 1, activity neither dies out nor explodes; it reverberates.
Beggs and Plenz (2003) recorded the spontaneous activity of the cerebral cortex in rats and found that the activity formed cascade-like patterns, the so-called neuronal avalanches, with a branching ratio close to 1. The brain seemed to live at a critical point. In humans, the branching ratio σ once again appears close to unity (Wang et al., 2025; Plenz et al., 2021; Wilting & Priesemann, 2019).
At the critical point, systems simultaneously exhibit maximal sensitivity to perturbations (responsiveness), maximal dynamic capacity (number of accessible states), maximal information transmission, and maximal complexity (Timme et al., 2016; Shew et al., 2009, 2011).
What Is the Branching Ratio and How Is It Measured?
Conceptually, the branching ratio is trivial: if at instant t there are A(t) active neurons and at t+1 there are A(t+1), then:
σ = ⟨ A(t+1) / A(t) ⟩
Three regimes follow from this (de Carvalho & Prado, 2000; Haldeman & Beggs, 2005):
Subcritical (σ < 1): activity decays; the system "forgets" the perturbation quickly. It is stable but poor in memory and not very expressive.Supercritical (σ > 1): activity explodes into cascades. This is the signature of pathological regimes such as epileptic seizures (Hsu et al., 2008; Hagemann et al., 2021).Critical (σ ≈ 1): each spike, on average, generates another spike. Activity reverberates, neuronal avalanches obey power laws, and the system maintains a structured memory of the input.
The beauty of σ is that it is a single number that summarizes the global dynamical regime. But measuring it is less trivial. When applied to in vivo cortical recordings, the measurement reveals that the cortex does not operate exactly at σ = 1, but slightly below, in a regime that the authors call reverberating (Wilting et al., 2018). The difference is important: being exactly at σ = 1 would be like pedaling a bicycle balanced on a tightrope; being slightly below allows for rapid adjustment to task demands without the risk of runaway explosion.
Criticality in Artificial Neural Networks: From the Edge of Chaos to Reservoir Computing
Bertschinger and Natschläger (2004) showed that random recurrent threshold networks reach their maximal computational capacity on temporal processing tasks precisely at the order–chaos transition.
Boedecker et al. (2012) extended the analysis to echo state networks within the reservoir computing paradigm, confirming that information transfer capacity and active memory are maximized at the edge of chaos.
Fig. 3. A spiking neuromorphic network with synaptic plasticity self-organizes toward criticality under low external input, exhibiting power-law avalanche size distributions — the hallmark of the critical state in both biological and artificial neural networks. Under higher input, the network shifts to a subcritical regime with truncated distributions. Reproduced from Cramer et al. (2020), Nature Communications, 11, 2853. CC BY 4.0.
In the language of artificial neural networks, the measurement parameter is called the spectral radius. When it exceeds 1, trajectories diverge exponentially (chaos); when it is well below 1, the network collapses to the fixed point and loses memory. The spectral radius close to 1 is, in this context, the formal equivalent of the biological σ ≈ 1 (Magnasco, 2022; Morales et al., 2023). In spiking neural networks, the branching ratio can be measured with methods almost identical to those used in neuronal cultures (Cramer et al., 2020; Zeraati et al., 2024).
Why Does Brain Criticality Maximize Neural Computation?
Operating close to σ ≈ 1 provides four advantages that are central to both the critical brain hypothesis and the design of brain-inspired AI systems:
Maximal dynamic range. Shew et al. (2009) showed that the range of input intensities the cortex can discriminate is maximal when the excitation–inhibition balance places the network at criticality.Maximized information capacity. The entropy of avalanche patterns and the mutual information between input and output peak at σ ≈ 1 (Shew et al., 2011).Optimal fading memory. In the critical regime, the perturbation is sustained just long enough to influence processing without contaminating the distant future; it is the sweet spot between stability and temporal integration (Boedecker et al., 2012).Complexity as a unifying measure. Timme et al. (2016) demonstrated that neural complexity is maximized exactly at the critical point, linking criticality with formal theories of consciousness and processing.
Fig. 4. Four computational advantages of operating near the critical branching ratio (σ ≈ 1). At criticality, neural networks achieve maximal dynamic range, maximized information capacity, optimal fading memory, and maximum complexity — properties that are central to both the critical brain hypothesis and brain-inspired AI design.
The Brain Does Not Always Operate at σ = 1
This does not imply that the brain always operates at σ = 1. Evidence rather suggests a slightly subcritical and modulable regime: during demanding tasks the network approaches criticality, during deep sleep it moves away, and pathological states (epilepsy, deep anesthesia, certain psychiatric conditions) are associated with measurable deviations from this operational range (Meisel et al., 2017; Zimmern, 2020). The branching ratio is becoming a dynamic biomarker of the functional state of the nervous system.
Why We Use the Branching Ratio in Neuraxon: Bioinspired AI Design at the Edge of Chaos
Neuraxon is a bioinspired system that adopts dynamical principles of the cortex as design constraints. The branching ratio is one of the most important, and we use it for four reasons:
As a Real-Time Operational Invariant for Neural Network Stability
In deep spiking or recurrent architectures, the dual risk of activity collapse (silent network, vanishing gradients) and runaway explosion (saturation, exploding gradients) is structural. Monitoring σ in real time gives us a single diagnostic scalar, independent of the concrete architecture, that indicates whether the system is alive in the computational sense.
As a Bioinspired Self-Regulation Target Through Self-Organized Criticality
The network self-organizes toward criticality without the need for centralized fine-tuning, replicating the principle of self-organized criticality (Bornholdt & Röhl, 2003; Levina et al., 2007). This drastically reduces sensitivity to hyperparameters and endows the system with robustness against distribution shifts. As we explored in NIA Volume 7 on artificial life and digital ecosystems, this is exactly how emergent complexity arises from local rules without centralized control.
Fig. 5. Neuraxon 3D network during active simulation, showing cascading activity across ternary-state neurons. Brightly active nodes (pink) propagate signals through excitatory (green) and inhibitory (pink) connections while other neurons remain at rest (gray), illustrating a reverberating regime near the critical branching ratio (σ ≈ 1). This balanced state — neither silent nor explosive — is what Neuraxon self-organizes toward using bioinspired criticality principles. Explore the interactive demo athuggingface.co/spaces/DavidVivancos/Neuraxon. Source: Qubic Scientific Team.
As a Bridge Between Neuroscientific Observation and AI Design
The branching ratio is one of the very few magnitudes that is measured with the same formalism in electrophysiology, fMRI, and artificial networks. This allows for testing bidirectional hypotheses: if an intervention improves biological criticality, we can ask whether the same intervention — translated into the artificial architecture — improves the model's computation, and vice versa. This principle is central to the neuromodulation framework and the astrocytic gating mechanisms we have developed in previous volumes of this academy.
As a Functional, Not Aesthetic, Criterion for Brain-Inspired AI
Criticality is an operational constraint with empirical consequences. Operating near the reverberating regime improves — as measured in our internal evaluations and submitted publications — generalization capacity, stability under input perturbations, representational richness, and the temporal coherence of reasoning. These effects qualitatively match those reported in both the biological (Cocchi et al., 2017) and artificial (Cramer et al., 2020; Morales et al., 2023) literature.
The Branching Ratio: From Statistical Physics to Brain-Inspired AI Architecture
The branching ratio is one of those conceptual rara avis: simple enough to reduce to a single formula, deep enough to bridge statistical physics, neuroscience, AI, and systems design. For the biological brain, σ ≈ 1 seems to be the regime where the virtuous combination of sensitivity, memory, expressiveness, and robustness emerges. For artificial networks, the same frontier — rebranded as the edge of chaos — predicts maximal computational capacity.
And for Neuraxon, it is a guiding principle of bioinspired design: an auditable, self-regulating, and biologically meaningful metric that helps us keep the system alive, in the richest sense of the word.
References
Beggs, J. M., & Plenz, D. (2003). Neuronal avalanches in neocortical circuits. The Journal of Neuroscience, 23(35), 11167–11177. https://doi.org/10.1523/JNEUROSCI.23-35-11167.2003Bertschinger, N., & Natschläger, T. (2004). Real-time computation at the edge of chaos in recurrent neural networks. Neural Computation, 16(7), 1413–1436. https://doi.org/10.1162/089976604323057443Boedecker, J., Obst, O., Lizier, J. T., Mayer, N. M., & Asada, M. (2012). Information processing in echo state networks at the edge of chaos. Theory in Biosciences, 131(3), 205–213. https://doi.org/10.1007/s12064-011-0146-8Bornholdt, S., & Röhl, T. (2003). Self-organized critical neural networks. Physical Review E, 67(6), 066118. https://doi.org/10.1103/PhysRevE.67.066118Cocchi, L., Gollo, L. L., Zalesky, A., & Breakspear, M. (2017). Criticality in the brain: A synthesis of neurobiology, models and cognition. Progress in Neurobiology, 158, 132–152. https://doi.org/10.1016/j.pneurobio.2017.07.002Cramer, B., Stöckel, D., Kreft, M., Wibral, M., Schemmel, J., Meier, K., & Priesemann, V. (2020). Control of criticality and computation in spiking neuromorphic networks with plasticity. Nature Communications, 11, 2853. https://doi.org/10.1038/s41467-020-16548-3de Carvalho, J. X., & Prado, C. P. C. (2000). Self-organized criticality in the Olami-Feder-Christensen model. Physical Review Letters, 84(17), 4006–4009. https://doi.org/10.1103/PhysRevLett.84.4006Derrida, B., & Pomeau, Y. (1986). Random networks of automata: A simple annealed approximation. Europhysics Letters, 1(2), 45–49. https://doi.org/10.1209/0295-5075/1/2/001Hagemann, A., Wilting, J., Samimizad, B., Mormann, F., & Priesemann, V. (2021). Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex. PLOS Computational Biology, 17(3), e1008773. https://doi.org/10.1371/journal.pcbi.1008773Haldeman, C., & Beggs, J. M. (2005). Critical branching captures activity in living neural networks and maximizes the number of metastable states. Physical Review Letters, 94(5), 058101. https://doi.org/10.1103/PhysRevLett.94.058101Hsu, D., Chen, W., Hsu, M., & Beggs, J. M. (2008). An open hypothesis: Is epilepsy learned, and can it be unlearned? Epilepsy & Behavior, 13(3), 511–522. https://doi.org/10.1016/j.yebeh.2008.05.007Langton, C. G. (1990). Computation at the edge of chaos: Phase transitions and emergent computation. Physica D: Nonlinear Phenomena, 42(1–3), 12–37. https://doi.org/10.1016/0167-2789(90)90064-VLevina, A., Herrmann, J. M., & Geisel, T. (2007). Dynamical synapses causing self-organized criticality in neural networks. Nature Physics, 3(12), 857–860. https://doi.org/10.1038/nphys758Magnasco, M. O. (2022). Robustness and flexibility of neural function through dynamical criticality. Entropy, 24(5), 591. https://doi.org/10.3390/e24050591Meisel, C., Klaus, A., Vyazovskiy, V. V., & Plenz, D. (2017). The interplay between long- and short-range temporal correlations shapes cortex dynamics across vigilance states. The Journal of Neuroscience, 37(42), 10114–10124. https://doi.org/10.1523/JNEUROSCI.0448-17.2017Morales, G. B., di Santo, S., & Muñoz, M. A. (2023). Unveiling the intrinsic dynamics of biological and artificial neural networks: From criticality to optimal representations. Frontiers in Complex Systems, 1, 1276338. https://doi.org/10.3389/fcpxs.2023.1276338Plenz, D., Ribeiro, T. L., Miller, S. R., Kells, P. A., Vakili, A., & Capek, E. L. (2021). Self-organized criticality in the brain. Frontiers in Physics, 9, 639389. https://doi.org/10.3389/fphy.2021.639389Shew, W. L., Yang, H., Petermann, T., Roy, R., & Plenz, D. (2009). Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. The Journal of Neuroscience, 29(49), 15595–15600. https://doi.org/10.1523/JNEUROSCI.3864-09.2009Shew, W. L., Yang, H., Yu, S., Roy, R., & Plenz, D. (2011). Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. The Journal of Neuroscience, 31(1), 55–63. https://doi.org/10.1523/JNEUROSCI.4637-10.2011Spitzner, F. P., Dehning, J., Wilting, J., Hagemann, A., Neto, J. P., Zierenberg, J., & Priesemann, V. (2021). MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity. PLOS ONE, 16(4), e0249447. https://doi.org/10.1371/journal.pone.0249447Timme, N. M., Marshall, N. J., Bennett, N., Ripp, M., Lautzenhiser, E., & Beggs, J. M. (2016). Criticality maximizes complexity in neural tissue. Frontiers in Physiology, 7, 425. https://doi.org/10.3389/fphys.2016.00425Turrigiano, G. G. (2008). The self-tuning neuron: Synaptic scaling of excitatory synapses. Cell, 135(3), 422–435. https://doi.org/10.1016/j.cell.2008.10.008Wang, J., Cao, R., Brunton, B. W., Smith, R. E. W., Buckner, R. L., & Liu, T. T. (2025). Genetic contributions to brain criticality and its relationship with human cognitive functions. Proceedings of the National Academy of Sciences, 122(26), e2417010122. https://doi.org/10.1073/pnas.2417010122Wilting, J., Dehning, J., Pinheiro Neto, J., Rudelt, L., Wibral, M., Zierenberg, J., & Priesemann, V. (2018). Operating in a reverberating regime enables rapid tuning of network states to task requirements. Frontiers in Systems Neuroscience, 12, 55. https://doi.org/10.3389/fnsys.2018.00055Wilting, J., & Priesemann, V. (2018). Inferring collective dynamical states from widely unobserved systems. Nature Communications, 9, 2325. https://doi.org/10.1038/s41467-018-04725-4Wilting, J., & Priesemann, V. (2019). 25 years of criticality in neuroscience — Established results, open controversies, novel concepts. Current Opinion in Neurobiology, 58, 105–111. https://doi.org/10.1016/j.conb.2019.08.002Yu, C. (2022). Toward a unified analysis of the brain criticality hypothesis: Reviewing several available tools. Frontiers in Neural Circuits, 16, 911245. https://doi.org/10.3389/fncir.2022.911245Zeraati, R., Engel, T. A., & Levina, A. (2024). Estimating intrinsic timescales and criticality from neural recordings: Methods and pitfalls. Current Opinion in Neurobiology, 86, 102871. https://doi.org/10.1016/j.conb.2024.102871Zimmern, V. (2020). Why brain criticality is clinically relevant: A scoping review. Frontiers in Neural Circuits, 14, 54. https://doi.org/10.3389/fncir.2020.00054
Explore the Complete Neuraxon Intelligence Academy
This is Volume 8 of the #Neuraxon Intelligence #academy by the #Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon, #aigarth , and Qubic's approach to brain-inspired, #decentralized artificial intelligence:
NIA Vol. 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Vol. 2: Ternary Dynamics as a Model of Living Intelligence — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Vol. 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Vol. 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.NIA Vol. 5: Astrocytes and Brain-Inspired AI — How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.NIA Vol. 6: Conscious Machines vs Intelligent Organisms: AI Consciousness Explained — Explores AI consciousness through the lens of Global Workspace Theory, Integrated Information Theory, and predictive coding.NIA Vol. 7: Conway's Game of Life, Artificial Life, and Digital Ecosystems — The science behind Qubic, Aigarth, and Neuraxon's approach to emergent complexity and self-organized criticality in decentralized AI.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org. Join the discussion on X, Discord, and Telegram.

Neuraxon Intelligence Academy — Volume 11
By the #Qubic Scientific Team
In brief: There are few expressions repeated so much and defined so little as "artificial general intelligence." This volume examines why every #AGI definition on offer says something different, why narrow #AI that beats humans has never felt like general intelligence, and why the most useful clue we have is that intelligence is not a score but a viable system of systems, the principle that guides our work on #Neuraxon and #aigarth .
There are few expressions repeated so much and defined so little as «artificial general intelligence». We use it as if it were a frontier that some system will cross on a specific day. When we try to pin down what exactly lies on the other side, however, the consensus evaporates.
Why Every Lab Has a Different Definition of AGI
Each laboratory and company offers its own definition, and each definition resembles the capabilities that the laboratory already masters or promises to master soon.
For some, general intelligence is to equal the human being in any task.
For others, to surpass the human in economically valuable work.
For still others, to reach the majority of cognitive tasks.
The three formulations sound reasonable. The three say different things, and none withstands much scrutiny.
This is not an abstract quibble. OpenAI's charter defines AGI as highly autonomous systems that outperform humans at most economically valuable work, while other leading labs anchor the same word to matching humans across most cognitive tasks. As independent analyses of these competing definitions have noted, when the same term is stretched to fit each organization's economic arrangements, disagreement about whether we have "reached AGI" becomes inevitable, the debaters are not looking at the same finish line.
Academic Attempts to Define Machine Intelligence Without Anthropocentrism
Academic research has tried to escape anthropocentrism. Legg and Hutter (2007) proposed understanding intelligence as an agent's capacity to achieve goals across a wide variety of environments, an elegant definition precisely because it does not tie intelligence to resembling a human.
Wang (2019) shifted the emphasis toward adaptation, and defined it as a system's capacity to cope with insufficient knowledge and resources.
Chollet (2019) turned the question around: what matters is not accumulated skill, but the efficiency with which a system acquires new skills. Each of these proposals is more rigorous than any corporate slogan, and each still leaves the underlying problem open. There is no accepted standard for saying when a machine has arrived.
Defining AGI by Elimination: What General Intelligence Is Not
Perhaps the problem is that we seek a positive definition when the only thing we are clear about is the negative one. We know, with considerable certainty, what is not general intelligence.
A pocket calculator surpasses any human mathematician in arithmetic and it occurs to no one to call it intelligent.
A chess engine effortlessly defeats the best player on the planet and we consider it, at most, an extraordinarily refined tool.
An automatic translator handles dozens of languages that no polyglot would master in several lifetimes.
In all these cases there is superhuman performance and, at the same time, a total absence of generality.
Narrow performance that surpasses the human has existed for decades and never seemed to us like intelligence. Defining by elimination turns out, paradoxically, to be more honest: general intelligence is not the sum of many narrow competences, however impressive each one of them may be.
The Economic Turing Test: Measuring What Intelligence Does
Faced with this conceptual fog, some voices from the industry itself have proposed a more down-to-earth criterion, an economic variant of the old Turing test (1950).
Instead of arguing about what intelligence is, let us propose measuring what it does.
The idea shifts the question from the philosophical terrain to the occupational one. A system would have reached a relevant milestone on the day it could perform a real job, be hired to do it and be paid for it without anyone around it discovering that it is not human. It would not matter then whether it «understands» or «reasons» in some deep sense. What would matter is whether it sustains the performance long enough to become indistinguishable from a professional. The proposal has the virtue of being verifiable. It also has the limitation of confusing, once again, economic capacity with intelligence, and of relying on a social verdict rather than on a property of the system. A machine could pass that test and still remain, in essence, fragile outside the script for which it was prepared.
What Science Fiction Taught Us About Machine Intelligence (and Why It Misleads)
A good part of our intuition about these machines does not come from science, but from fiction, and fiction has bequeathed to us incompatible assumptions.
In Minority Report intelligence is anticipation: a system that predicts what is going to happen before it happens, to the point of acting on futures that do not yet exist.
In I, Robot intelligence is obedience to rules: creatures governed by explicit laws that, precisely because of their rigidity, end up producing consequences that no one had foreseen.
In 2001 intelligence is opacity: a machine that reasons impeccably and whose true motives remain, until the end, indecipherable.
In more recent stories, such as Ex Machina, the decisive test is no longer to solve problems, but to manipulate the observer; and in Her intelligence is measured by the intimacy and affection it is capable of awakening.
Each story installs in our head a different definition of what it means to be intelligent, to predict, to obey, to conceal, to seduce, and we drag those definitions along, without noticing, when we judge real systems. Cinema did not give us a theory of intelligence. It gave us a repertoire of expectations that contradict one another.
Intelligence in Nature: A Gradient, Not a Switch
If we look at nature, the panorama becomes even less clear-cut, and for good reasons. Intelligence does not appear in the living world as a switch that turns on or off, but as a gradient with very diverse forms.
A New Caledonian crow manufactures tools with which it extracts food and chains together several steps to solve a problem it had never seen before (Hunt, 1996).
An octopus distributes a good part of its nervous system through its arms and solves spatial problems with a bodily organization so different from ours that it is hard to find a common language to describe it (Godfrey-Smith, 2016).
A colony of bees chooses the location of a new nest through a collective process that no isolated individual could carry out, and it does so with notable reliability (Seeley, 2010).
None of these intelligences is reducible to the others, and none is «less» intelligence for not resembling the human one. What biology teaches us is that generality is not equivalent to a single scale where some stand higher than others, but to different ways of facing changing environments with limited resources.
Human Intelligence Is an Architecture, Not a Score
This lesson should make us cautious also with ourselves.
For more than a century we have tried to compress human intelligence into a number. Ever since Spearman (1904) described a general factor that seemed to influence almost all cognitive tests, the intelligence quotient became the convenient summary of something that does not let itself be summarized. And during that same century we have verified how little that number captures. Someone who obtains a high score is not necessarily the one who best negotiates a conflict, interprets an ambiguous situation or learns a new trade under pressure. The datum is stable; the life it claims to summarize is not. It would be a mistake, nonetheless, to go from excess to emptiness and conclude that intelligence lacks structure.
Decades of psychometric research, synthesized in the Cattell, Horn and Carroll model (Carroll, 1993; McGrew, 2009), point to something more interesting: human intelligence is organized hierarchically. At the apex there is a general factor that filters into almost everything we do. Below it, a handful of broad abilities, fluid reasoning, crystallized knowledge, working memory, processing speed. Further down still, dozens of specific abilities. It is neither a solitary number nor an archipelago of disconnected talents. It is an architecture. And it is worth retaining that word, architecture, because it will be the key to everything that follows.
(We traced the origins of that general factor across education, neuroscience, and AI in NIA Volume 9: The Origins of the g Factor, and examined how it breaks down when applied to machines in NIA Volume 10: How Do We Measure the Intelligence of a Machine?.)
Why AGI Benchmarks Fail: The Problem With Closed Worlds
If intelligence is architecture and not a score, then the tests with which we evaluate it matter enormously. Here lies one of the great self-deceptions of recent years.
We have celebrated that systems break record after record on standardized tests without noticing that almost all of them share the same flaw: they measure performance in closed worlds. Questions with a known answer, tasks with a fixed format, problems that someone already solved before. When a static test becomes popular, moreover, it becomes vulnerable: it is enough to generate thousands of attempts in parallel, or to train on similar tasks, to inflate the score without there being any true generalization.
But the intelligence that interests us manifests itself precisely where the world is open: when one must explore an unknown environment, build on the fly a model of how it works, infer what the goal is and chain actions toward it, correcting course when conditions change.
That is why the new generations of interactive tests, of the kind posed by ARC-AGI-3 (ARC Prize Foundation, 2026; Chollet, 2019), are so revealing. They do not present a puzzle and await an answer. They place the system inside an environment whose rules are discovered only by acting, turn by turn, without prior instructions. What they evaluate is not how much it knows, but how efficiently it learns something truly new. The contrast is telling: in its early versions, people solve almost all of these challenges while frontier models barely scratch a minimal fraction. It is a difference of nature, not of degree, and it reorients the entire conversation.
From "Is It Smart?" to "Is It Viable?": Stafford Beer's Better Question
Having reached this point, it is worth changing the question.
For too long we have asked whether a machine is «smart».
The cybernetician Stafford Beer, decades ago now, proposed a more fertile question for complex systems: not whether they are smart, but whether they are viable (Beer, 1981, 1985). A viable system is one capable of sustaining itself, preserving its identity and remaining governable while its environment changes ceaselessly. Beer maintained that any system that survives in a complex world, an organism, a company, a state, must house certain indispensable functions: units that carry out the task, mechanisms that coordinate them, instances that regulate internal resources, a capacity to scan the exterior and anticipate change, and a core that preserves the purpose of the whole. What is decisive about his model is that these functions repeat at different scales, like Russian dolls: each viable part contains viable parts and, at the same time, forms part of a viable whole. Read this way, intelligence ceases to be a property that an object possesses and becomes a property that a system sustains.
General Intelligence as a Network of Networks
This is, in our judgment, the most valuable clue we have.
If general intelligence is not the sum of narrow competences, nor a number, nor a record in a closed world, but the viability of a system that organizes itself at different scales, then it is improbable that it will arrive at the hand of a single gigantic model that one day crosses an invisible line. It is far more plausible that it will emerge from the interaction of many pieces: agents that specialize and coordinate, memories that persist, modules that evaluate and correct, networks that contain other networks. The idea is not new; Minsky (1986) already imagined the mind as a society of simple processes, none intelligent on its own, whose organization gave rise to something that was. Generality would then be a collective and not an individual phenomenon, something that appears between the components and not within any of them. The threshold we so eagerly seek does not exist as a line. It exists, if at all, as the moment when a network of networks begins to behave as a coherent whole.
How Neuraxon and Aigarth Pursue General Intelligence
It is precisely this intuition that guides our work. If intelligence is architecture, it is worth studying architectures; and if it emerges from networks that organize themselves at different scales, it is worth building, as a computational simulation, networks of simple units capable of coordinating, remembering, valuing and planning.
That is the logic we pursue with Aigarth and with the Neuraxons: not a model that imitates human language from the outside, but a system of systems inspired by the principles through which nervous tissue solves, in its own way, the problem of adapting to a world that never stops changing. We do not thereby claim to be approaching any form of consciousness or any ultimate mystery of the mind. We claim, more modestly and more ambitiously at once, that the path toward a truly general intelligence goes through understanding and simulating how many small pieces, suitably organized, come to sustain something that none of them contains on its own.
(This "third path" between biological and artificial networks is the subject of NIA Volume 4: Neural Networks in AI and Neuroscience, and the emergence of complexity from simple local rules runs through NIA Volume 7: Conway's Game of Life, Artificial Life, and Digital Ecosystems.)
Why a Single Superintelligence Cannot Replace a Society
It is worth, however, resisting one last temptation, the most seductive of all.
Suppose for a moment that this path succeeded and that we had a general, powerful and reliable system. It would be natural to imagine it then as a supreme instance, an oracle capable of taking for us the decisions that today overwhelm us. That image, attractive as it is, rests on an error that has nothing to do with the power of the machine, but with the nature of knowledge.
There is not, strictly speaking, a superior knowledge stored somewhere awaiting a sufficiently large processor. Hayek (1945) formulated it with a clarity that time has not belied: the knowledge that a society needs in order to function is not concentrated in any mind, but dispersed among millions of people, in large part tacit, tied to circumstances of place and moment that never come to be put in writing. That knowledge cannot be centralized because it does not exist in transferable form; it is created and revised in the very action of those who possess it.
Cybernetics arrived at the same conclusion by another route. Ashby's (1956) law of requisite variety, on which Beer built much of his thought, establishes that only variety can absorb variety: no single controller can match the diversity of states of a system more complex than itself. A society generates, at every instant, far more possible situations than any center could inspect and regulate. To place a single intelligence at the apex of that system would not be the height of viability, but its negation: a single point of control confronting a variety that exceeds it by definition, exactly the opposite of the recursive and distributed architecture that makes a whole viable.
There is, moreover, an obstacle that no increase in computation dissolves. Human systems are not closed mechanisms that can be solved from outside; they are adaptive orders in which agents learn, imitate, compete, err and react to what is said about them (Holland, 1995; Arthur, 2021). This reflexivity has an uncomfortable consequence: any prediction or any rule influential enough alters the behavior it meant to describe. A metric that becomes a target ceases to measure what it measured; a broadcast forecast becomes a prophecy that fulfills or belies itself. There is no stable, external observation point from which to compute the optimum, because the very act of intervening displaces the target. The uncertainty that surrounds these systems is not a lack of data that more information will remedy; it is structural. Their future is not computed: it is made.
And even if knowledge were complete and the system were not reflexive, the decisive thing would remain: the ends are in dispute. A plural society does not have a single correct objective function that an optimizer could maximize on its behalf. Disagreement, trial, error and correction are not defects of the social process, but the very mechanism by which a society discovers and readjusts its own answers. Under the right conditions, the diversity of perspectives solves problems better than any individual solver, however brilliant (Page, 2007). To replace that process with a single decision-maker would not perfect society: it would freeze precisely what allows it to adapt, and would eliminate the redundancy that makes it robust against error.
The Honest Role of Artificial Intelligence in a Viable Society
From all of this there follows an honest, and by no means modest, role for artificial intelligence, however general it may come to be.
It can help us see patterns that escape us, simulate scenarios before committing to them, refine concrete decisions and make them with less blindness. What it cannot do is take the place of the collective, fallible and self-correcting process through which we learn as a society.
If intelligence is a viable system of systems, so too is a society; and an artificial intelligence, however capable, is one more component within that system, never its apex.
References
ARC Prize Foundation. (2026). ARC-AGI-3: A new challenge for frontier agentic intelligence [Technical report]. arXiv. arcprize.org/arc-agi/3Arthur, W. B. (2021). Foundations of complexity economics. Nature Reviews Physics, 3(2), 136–145.Ashby, W. R. (1956). An introduction to cybernetics. Chapman & Hall.Beer, S. (1981). Brain of the firm (2nd ed.). Wiley.Beer, S. (1985). Diagnosing the system for organizations. Wiley.Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press.Chollet, F. (2019). On the measure of intelligence (arXiv:1911.01547) [preprint]. arXiv.Godfrey-Smith, P. (2016). Other minds: The octopus, the sea, and the deep origins of consciousness. Farrar, Straus and Giroux.Hayek, F. A. (1945). The use of knowledge in society. The American Economic Review, 35(4), 519–530.Holland, J. H. (1995). Hidden order: How adaptation builds complexity. Addison-Wesley.Hunt, G. R. (1996). Manufacture and use of hook-tools by New Caledonian crows. Nature, 379(6562), 249–251.Legg, S., & Hutter, M. (2007). Universal intelligence: A definition of machine intelligence. Minds and Machines, 17(4), 391–444.McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10.Minsky, M. (1986). The society of mind. Simon & Schuster.Page, S. E. (2007). The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton University Press.Seeley, T. D. (2010). Honeybee democracy. Princeton University Press.Spearman, C. (1904). «General intelligence,» objectively determined and measured. The American Journal of Psychology, 15(2), 201–292.Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37.
Explore the Full Neuraxon Intelligence Academy Series
This is Volume 11 of the Neuraxon Intelligence Academy by the Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon, Aigarth, and Qubic's approach to brain-inspired, decentralized artificial intelligence:
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.NIA Volume 5: Astrocytes and Brain-Inspired AI — How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.NIA Volume 6: Conscious Machines vs Intelligent Organisms: AI Consciousness Explained — Explores AI consciousness through the lens of Global Workspace Theory, Integrated Information Theory, and predictive coding.NIA Volume 7: Conway's Game of Life, Artificial Life, and Digital Ecosystems — The science behind Qubic, Aigarth, and Neuraxon's emergent complexity and self-organized criticality.NIA Volume 8: Brain Criticality and the Branching Ratio in Neural and Artificial Networks — Why a branching ratio near 1 and self-organized criticality are bioinspired design principles in Neuraxon.NIA Volume 9: The Origins of the g Factor: From Education and Neuroscience to Artificial Intelligence — Explores the origins of the g factor across education, neuroscience, and AI.NIA Volume 10: How Do We Measure the Intelligence of a Machine? The g Factor, ARC-AGI, and the Future of AI Evaluation — The g factor, François Chollet's ARC-AGI benchmark, data contamination in LLM evaluation, and why skill-acquisition efficiency is the real test of intelligence.
Qubic is a decentralized, open-source network. To learn more, visit qubic.org. Join the discussion on X, Discord, and Telegram. Nothing on this site should be construed as investment, legal, or financial advice.