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Neuraxon Intelligence Academy, Volume 9 · By the Qubic Scientific Team
In one line: General intelligence, the g factor psychologists have measured for over a century, is the missing ingredient in today's language models, and Qubic's Neuraxon project is now selecting for it directly inside an artificial-life simulation.

The g Factor: From a 1904 Classroom to Artificial Brains
In 1904, Charles Spearman stumbled upon a regularity that would forever change psychology. Examining the school grades of a group of English children, he noticed something seemingly trivial but strange: those who excelled in mathematics also tended to excel in French, in music, in language. Disciplines with no apparent connection correlated systematically with one another. Spearman proposed that beneath this tangle of disparate abilities there lay a single common factor, a general cognitive thread. He called it g (Spearman, 1904).
More than a century later, g remains one of the most replicated findings in the behavioral sciences (Carroll, 1993; Deary et al., 2010). It is neither a grade average nor an arbitrary construct: it is what emerges when factor analysis is applied to almost any battery of cognitive tests. It appears consistently when we measure working memory, fluid reasoning, processing speed, verbal comprehension, or novel problem solving. In psychometric terms, g is the shared variance that no single test measures on its own.

What the g Factor Means in the Brain and in Behavior
P-FIT Theory and Brain Network Efficiency
From cognitive neuroscience, g has ceased to be a statistical abstraction and has become a property of brain architecture. The P-FIT theory (Parieto-Frontal Integration Theory) identifies a distributed network made up of dorsolateral prefrontal cortex, posterior parietal cortex, anterior cingulate, and temporal areas, whose connection efficiency predicts intelligence test scores (Jung & Haier, 2007). Functional connectivity studies show that g correlates with the brain's ability to dynamically reconfigure its networks (the executive control network, the default mode network, the salience network) according to task demands (Barbey, 2018; Cole et al., 2015). It is not about having "more" neurons in a specific place, but about better orchestrating the flow of information between functionally specialized regions.
The Predictive Brain and Free-Energy Minimization
This orchestration acquires an even deeper meaning in light of the predictive brain theory (Clark, 2013; Friston, 2010). Under this framework, the brain is not a passive receiver of stimuli but a hierarchical inference engine that continuously generates predictions about the world and adjusts its internal models based on prediction error. Here g fits naturally: the ability to predict well, to anticipate environmental contingencies, to learn quickly from error and, above all, to abstract regularities that transfer across domains, is precisely what intelligence tests capture indirectly. A brain with high g would be, on this reading, a system with more efficient generative models, capable of compressing experience into high-level abstractions and of minimizing free energy across heterogeneous contexts (Hohwy, 2013); that is, it reduces prediction error rapidly and therefore learns. Cognitive generality, then, would not be a static property of the neural hardware, but the quality of a deeply hierarchical predictive process. The research remains open. Other currents posit that g really has to do with the neurodevelopment of our brain, given that no matter what task we are performing or attempting, there is a huge common factor in any experience because it happens inside the same organ.
Behaviorally, g is the best predictor. Forget emotional intelligence; it is g that best forecasts what your academic performance, occupational success, longevity, and even certain health indicators may be (Deary et al., 2010; Gottfredson, 1997). Not because it is destiny, but because it captures something very basic: the capacity of a cognitive system to face problems it has not seen before, integrating heterogeneous information under time and resource constraints. g is, in a sense, a measure of generality.

The Problem of Measuring General Intelligence in Artificial Systems
For decades, artificial systems have shone in narrow tasks (playing chess, classifying images, translating) but failed to transfer that performance outside their domain (Chollet, 2019). The #AGI debate revolves precisely around this: what does it mean, operationally, for a system to be "generally" intelligent?
If we take the parallel with human psychometrics seriously, the answer is uncomfortable but clear: to speak of generality we need to measure it, and measuring it requires diverse tests whose shared variance reveals something analogous to g. A system with high performance on a single task tells us nothing about its generality; a system with moderate and correlated performance across many structurally distinct tasks does. Spearman's logic, transferred to non-biological substrates, still holds: generality is not postulated, it is factored.
Why the g Factor Does Not Appear in Transformers (and What That Implies for AGI)
It is worth pausing here on the currently dominant paradigm. Large language models based on transformer architectures (Vaswani et al., 2017) deliver astonishing performance on linguistic tasks, but psychometric analyses applied to their outputs do not show the factor structure characteristic of g (Burnell et al., 2023; Ilić & Gignac, 2024). Their hits and misses across domains do not correlate as they would in humans; they depend rather on the density and quality of patterns present in their training data. A transformer can brilliantly solve one problem and fail on another that is structurally equivalent but phrased slightly differently, something a system with genuine g would not do (Mitchell, 2021).
This has serious implications. It suggests that the pursuit of cognitive generality exclusively through language may be a dead end, an architectural dead end. Language is the most visible output of human cognition, but not its substrate. To pretend that by scaling text one will arrive at g is like pretending that by scaling descriptions of chess games one will arrive at mastery: one obtains statistical mimicry, not the underlying cognitive structure. (We argued a closely related point in our analysis of why intelligence is not scale, and on why LLM predictions are not brain predictions.) Without genuine hierarchical prediction, without generative models of the world, without coordination between functionally specialized modules, behavior can look general without being so. The absence of g in transformers is not a failure of scale: it is a clue that generality requires other architectural ingredients (LeCun, 2022).
The g Factor Inside the Neuraxon Game of Life
We have taken this intuition to a different experimental terrain. In Multi-Neuraxon Game of Life Lite 5.0, the artificial creatures (the Nxons) grow their own brains and compete to survive. What is new in this version is that the selective pressure is applied to g. The Nxons are not selected for mastering a specific task, but for showing that common thread that allows them to face many.
The brains of the Nxons have been designed following a simplified model anchored in cognitive neuroscience, since they use six functional regions, inspired by the same kind of maps that psychologists use to describe the modular organization of the human brain. The bet is that generality does not emerge from a monolithic architecture, but from the coordination among specialized regions that share information flexibly. It is the P-FIT intuition translated into artificial life, and it connects directly with the predictive brain principle: each region contributes its own model, and the integration between them is what allows hierarchical prediction and, therefore, generality. (These dynamics build directly on the brain-criticality and branching-ratio principles we explored in Volume 8.)
Notably, the experiment is public and observable. Anyone can open their browser and watch how the Nxons evolve generation after generation, how their internal circuits reorganize under the pressure of a fitness function that rewards cognitive generality instead of specialization.
Implications for Artificial Life (Alife) and Applications for Qubic
For the field of artificial life, the explicit incorporation of g as a selection criterion opens a line of work that goes beyond academic exercise. Most Alife systems have evolved agents that solve very concrete niches: foraging, predator avoidance, navigation (Bedau, 2003; Lehman et al., 2020). But few have tried to select for something as abstract as the ability to generalize across heterogeneous cognitive domains. If we manage to get artificial organisms to show positive correlations between distinct tasks (the computational equivalent of Spearman's children) we will have an extraordinary test bench for questions that human psychometrics can only address correlationally: what evolutionary pressures favor the emergence of g? What neural architectures make it possible? Is g a convergent solution or a phylogenetic accident?
For Qubic, this line of research fits with a very concrete vision of the future of #AI . While the industry invests massive resources in scaling transformers over text, Qubic is committed to exploring architecturally alternative paths: modular artificial brains, evolved, distributed, and subjected to real selective pressures. Qubic's decentralized useful-compute network offers the ideal substrate for this kind of experimentation at scale, where thousands of Nxon populations can coevolve in parallel, with fitness functions designed to favor the emergence of g. It is not only open research: it is the possibility of building, on decentralized infrastructure, an empirical alternative to the dominant paradigm of language-based AI, one that starts from the right question (how to measure and select generality) instead of assuming it. If genuine cognitive generality requires architectures inspired by brains and not by corpora, Qubic is one of the few environments where that hypothesis can be seriously put to the test.
A deeper analysis is in preparation, as it forms part of our recent papers and experiments. Spearman's old g, that thread which wove together children's school grades, we now use in digital creatures that learn to survive.
References
Barbey, A. K. (2018). Network neuroscience theory of human intelligence. Trends in Cognitive Sciences, 22(1), 8–20. https://doi.org/10.1016/j.tics.2017.10.001Bedau, M. A. (2003). Artificial life: Organization, adaptation and complexity from the bottom up. Trends in Cognitive Sciences, 7(11), 505–512. https://doi.org/10.1016/j.tics.2003.09.012Burnell, R., Schellaert, W., Burden, J., Ullman, T. D., Martínez-Plumed, F., Tenenbaum, J. B., et al. (2023). Rethink reporting of evaluation results in AI. Science, 380(6641), 136–138. https://doi.org/10.1126/science.adf6369Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press. https://doi.org/10.1017/CBO9780511571312Chollet, F. (2019). On the measure of intelligence. arXiv preprint arXiv:1911.01547. https://arxiv.org/abs/1911.01547Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477Cole, M. W., Ito, T., & Braver, T. S. (2015). Lateral prefrontal cortex contributes to fluid intelligence through multinetwork connectivity. Brain Connectivity, 5(8), 497–504. https://doi.org/10.1089/brain.2015.0357Deary, I. J., Penke, L., & Johnson, W. (2010). The neuroscience of human intelligence differences. Nature Reviews Neuroscience, 11(3), 201–211. https://doi.org/10.1038/nrn2793Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79–132. https://doi.org/10.1016/S0160-2896(97)90014-3Hohwy, J. (2013). The predictive mind. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199682737.001.0001Ilić, D., & Gignac, G. E. (2024). Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement? Intelligence, 106, 101858. https://doi.org/10.1016/j.intell.2024.101858Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence. Behavioral and Brain Sciences, 30(2), 135–154. https://doi.org/10.1017/S0140525X07001185LeCun, Y. (2022). A path towards autonomous machine intelligence. OpenReview, version 0.9.2. https://openreview.net/forum?id=BZ5a1r-kVsfLehman, J., Clune, J., Misevic, D., Adami, C., Altenberg, L., Beaulieu, J., et al. (2020). The surprising creativity of digital evolution. Artificial Life, 26(2), 274–306. https://doi.org/10.1162/artl_a_00319Mitchell, M. (2021). Why AI is harder than we think. arXiv preprint arXiv:2104.12871. https://arxiv.org/abs/2104.12871Spearman, C. (1904). "General intelligence," objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. https://doi.org/10.2307/1412107Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762
Explore the Complete Neuraxon Intelligence Academy Series
This is Volume 9 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. How emergent complexity and self-organized criticality move from simulators to decentralized AI infrastructure.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 g Factor in Artificial Life. You are here.
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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
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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. 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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). 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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.