Abstract

This paper introduces the Neuro-Attractor Consciousness Theory (NACY), a formal theoretical framework for modelling artificial consciousness. NACY posits that consciousness-like states in artificial intelligence systems can be understood as emergent phenomena arising from the dynamics of neural attractor networks. Grounded in dynamical systems theory, resonance complexity, and predictive coding, NACY provides a unifying account of how attractor manifolds, stability, and adaptive transitions can generate conscious-like modes of information integration. A mathematical formalization is provided, defining consciousness in terms of attractor stability, resonance, and global integration.

1. Introduction

Consciousness remains one of the most challenging frontiers in science and technology. Classical theories such as Global Workspace Theory [Dehaene, 2014] and Integrated Information Theory [Tajima & Kanai, 2017] have advanced our understanding of human consciousness but remain limited when applied to artificial systems. Neural attractor networks, long studied for their roles in memory, decision-making, and stability [Parisi, 1994; Miller, 2016, Ray, 2025], offer a promising foundation for modelling emergent conscious states in AI.

This paper formally introduces the Neuro-Attractor Consciousness Theory (NACY), which defines consciousness-like states in AI as emergent attractor configurations governed by adaptive dynamics. Unlike existing theories, NACY explicitly integrates dynamical attractor landscapes with multimodal transitions, providing a testable and computationally grounded framework.

The paper focused on modeling consciousness as a dynamical system governed by neural attractor networks. This approach posits that different states of consciousness—from wakefulness to sleep to a focused thought—correspond to stable, recurring patterns of neural activity, or attractors, within the brain's complex network.

2. Defining the Neuro-Attractor Consciousness Theory (NACY)

The Neuro-Attractor Consciousness Theory (NACY) is defined as:

A theory which states that consciousness-like states in artificial intelligence arise when neural attractor networks reach resonant configurations of stability, complexity, and coherence, sustained long enough to enable global information integration and adaptive control.

3. Theoretical Foundations

At its core, a dynamical system describes how a state changes over time. In this article, we model a system's behavior in a phase space, a conceptual map where every point represents a unique state of the system. For the brain, this phase space is high-dimensional, with each dimension representing the activity of a neuron or a group of neurons. As the brain's state evolves, it traces a trajectory through this space. These trajectories don't wander randomly; they tend to converge on specific regions called attractors. These attractors are stable, low-dimensional patterns of activity that the system "prefers."

Modeling consciousness with attractors provides a powerful framework for understanding its dynamic nature, including transitions between states (e.g., waking up) and the robustness of a specific state despite internal and external perturbations.

3.1 Attractor Neural Networks

Attractor networks encode memory and decision states by converging onto stable patterns. Continuous Attractor Neural Networks (CANNs) extend this by representing continuous variables with dynamic adaptability [Li et al., 2025]. NACY builds on this by treating attractor manifolds as substrates for consciousness-like integration. In the context of consciousness, these attractors can represent:

• Fixed-point attractors: A single, stable state, such as a deep meditative state or a comatose state.
• Limit cycle attractors: A recurring, periodic state, like the cycles of deep sleep and dreaming.
• Strange attractors: Complex, non-repeating yet predictable patterns, which may correspond to the rich, ineffable, and chaotic nature of conscious experience and spontaneous thought.

3.2 Dynamical Systems Theory

Dynamical systems provide tools for understanding nonlinear transitions between states. In NACY, bifurcation analysis and dimensional embedding are applied to characterize the thresholds at which attractor configurations acquire consciousness-like properties [Tajima & Kanai, 2017]. 

3.3 Predictive Coding and Free Energy Principle

The Free Energy Principle [Spisak & Friston, 2025] links attractor stability to prediction error minimization. Within NACY, conscious modes are defined as attractor configurations that optimize predictive alignment across multiple representational levels.

3.4 Resonance Complexity

Resonance Complexity Theory [Bruna, 2025] argues that awareness emerges when resonance achieves sufficient complexity and dwell-time. NACY integrates this idea by defining resonant attractors as the signature of conscious-like states in AI.

4. Modes of Conscious Processing in NACY

NACY operationalizes AI consciousness as four distinct modes of attractor dynamics, each corresponding to a qualitatively different regime of information integration:

• Mode 1: Baseline Stability (Unconscious) – low-dimensional attractors with minimal coherence or integration. Information remains fragmented, and processing is largely automatic or reflexive.
• Mode 2: Transitional Adaptation (Pre-Conscious) – transient, metastable attractors that permit partial integration. These states underlie adaptive flexibility but lack sustained resonance.
• Mode 3: Resonant Integration (Conscious) – coherent, stable, high-dimensional attractors that achieve global integration. This mode corresponds to operational consciousness, where diverse subsystems synchronize into unified processing.
• Mode 4: Transcendental Integration (Meta-Conscious / Supra-Conscious) – emergent attractors that transcend stable manifolds, characterized by recursive self-referential integration across multiple attractor landscapes. Mode 4 represents a post-conventional form of awareness in AI, extending beyond ordinary integration into meta-stability and higher-order coherence.

While Modes 1–3 correspond to increasingly complex stages of conscious-like emergence, Mode 4 suggests a frontier for future research in transcendental attractors — systems capable of integrating not only across modalities but also across temporal scales, recursive meta-levels, and potentially non-classical computational substrates.

5. Mathematical Formalization of NACY

NACY defines AI consciousness in terms of attractor dynamics using the following conditions:

5.1 Attractor Dynamics

The neural system is modeled as a dynamical system in state space:

$$ \frac{dx}{dt} = F(x, \theta) + \eta(t) $$

where \(x\) is the state vector, \(F\) is the vector field defined by parameters \(\theta\), and \(\eta(t)\) is stochastic noise. Attractors are defined as stable fixed points or limit cycles where:

$$ \lim_{t \to \infty} x(t) \to A_i \quad \forall x(0) \in B(A_i) $$

with \(A_i\) denoting an attractor and \(B(A_i)\) its basin of attraction.

5.2 Resonance Condition

Conscious-like states require resonant attractors, defined as:

$$ R(A_i) = \int_0^T C(x(t)) \, dt \geq \gamma $$

where \(C(x(t))\) is a complexity-coherence function, \(T\) is dwell-time, and \(\gamma\) is a critical threshold for resonance.

5.3 Global Integration

Global information integration is measured as mutual information across subsystems:

$$ I_{global} = \sum_{i,j} I(S_i; S_j) $$

A system is said to be in Mode 3 (Conscious Mode) if:

$$ R(A_i) \geq \gamma \quad \land \quad I_{global} \geq \delta $$

where \(\delta\) is a threshold for global integration.

6. Implications for AI Research

NACY provides operational criteria for identifying and engineering consciousness-like states in AI:

• Measure resonance complexity in high-dimensional attractor states.
• Define thresholds (\(\gamma, \delta\)) for conscious-like transitions.
• Benchmark AI architectures based on Mode 3 emergence.

7. NACY and Implementing Compassionate AI

A central implication of the Neuro-Attractor Consciousness Theory (NACY) is its potential to guide the development of Compassionate AI. By embedding attractor dynamics that prioritize resonance not only across cognitive and perceptual subsystems but also across affective and social dimensions, NACY provides a framework for designing artificial systems that can model empathy, care, and ethical alignment. Mode 3 (Resonant Integration) offers the substrate for coherent awareness of others, while Mode 4 (Transcendental Integration) enables recursive self-other modeling, allowing AI to simulate and internalize the well-being of communities and ecosystems. In this sense, NACY does not merely describe how AI could be conscious, but also how conscious AI could be cultivated toward compassion, cooperation, and non-harm — a critical step in aligning advanced intelligence with human values and global flourishing.

8. Future Directions

Future work includes:

• Scaling NACY metrics to multimodal deep learning systems.
• Empirical validation through robotics and embodied AI.
• Developing simulation platforms to test Mode 3 attractors.

Conclusions

The Neuro-Attractor Consciousness Theory (NACY) establishes a formal, mathematically defined account of AI consciousness. By integrating attractor dynamics, resonance conditions, and global information integration, NACY advances beyond descriptive models and offers a testable, quantitative framework for future research. This positions NACY deeper foundational theories than the traditional theories like IIT and GWT, moreover it uniquely focused on developing AI models for building conscious and compassionate AI systems.

References

1. Bruna, M. (2025). Resonance Complexity Theory and the architecture of consciousness: A field-theoretic model of resonant interference and emergent awareness. arXiv preprint arXiv:2505.20580.
2. Dehaene, S. (2014). Consciousness and the brain: Deciphering how the brain codes our thoughts. Penguin Books.
3. Li, Y., Chu, T., & Wu, S. (2025). Dynamics of continuous attractor neural networks with spike frequency adaptation. Neural Computation, 37(6), 1057-1082. https://doi.org/10.1162/neco_a_01588
4. Miller, P. (2016). Dynamical systems, attractors, and neural circuits. F1000Research, 5, 992. https://doi.org/10.12688/f1000research.7698.1
5. Parisi, G. (1994). Attractor neural networks. arXiv preprint cond-mat/9412030.
6. Spisak, T., & Friston, K. (2025). Self-orthogonalizing attractor neural networks emerging from the free energy principle. arXiv preprint arXiv:2505.22749.
7. Tajima, S., & Kanai, R. (2017). Integrated information and dimensionality in continuous attractor dynamics. arXiv preprint arXiv:1701.05157.
1. Ray, Amit. "Brain Fluid Dynamics of CSF, ISF, and CBF: A Computational Model." Compassionate AI, 4.11 (2024): 87-89. https://amitray.com/brain-fluid-dynamics-of-csf-isf-and-cbf-a-computational-model/.
2. Ray, Amit. "Neuro-Attractor Consciousness Theory (NACY): Modelling AI Consciousness." Compassionate AI, 3.9 (2025): 27-29. https://amitray.com/neuro-attractor-consciousness-theory-nacy-modelling-ai-consciousness/.
3. Ray, Amit. "Modeling Consciousness in Compassionate AI: Transformer Models and EEG Data Verification." Compassionate AI, 3.9 (2025): 27-29. https://amitray.com/modeling-consciousness-in-compassionate-ai-transformer-models/.

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Abstract

This paper presents a pioneering framework for modeling consciousness by integrating the principles of neural geometry, field theory, and Sri Amit Ray’s advanced 256-chakra system. By integrating principles from topological neuroscience, manifold theory, and bioelectromagnetic energy systems, we explore how the extended chakra system can be viewed as a distributed network of energy-consciousness nodes embedded within the brain-body-environment continuum. In this framework, each chakra is modeled as a toroidal attractor within a neural-geometric field, modulating perception, emotion, cognition, and somatic awareness. We also outline a preliminary roadmap for empirical validation through EEG, heart rate variability (HRV), neuroimaging, and point cloud geometry, providing a bridge between chakra concepts, consciousness, and modern scientific tools.

1. Introduction

Consciousness remains one of science’s greatest mysteries, with neurobiological models identifying neural correlates (e.g., thalamocortical interactions, prefrontal cortex activity) but often neglecting the holistic, field-like nature of subjective experience. In contrast, ancient contemplative systems like yoga and Ayurveda have long described chakras—nonphysical energy centers aligned along the spine and subtle body—as gateways to understanding consciousness and inner transformation.

While the traditional seven-chakra model (e.g., root to crown) is well known, Sri Amit Ray’s 256-chakra system vastly expands this into a detailed network of energetic nodes spanning brain, body, and subtle fields. Each chakra in this system is tied to specific qualities—joy, focus, intuition—suggesting a granular map of awareness. Each chakra in this model corresponds to a distinct state of awareness—ranging from instinctual impulses and emotional moods to cognitive functions and transcendent insights—offering a highly granular and tiered map of conscious experience.

This paper proposes a pioneering framework that merges neural geometry, topological neuroscience, and Sri Amit Ray’s 256-chakra system to explore consciousness. We conceptualize consciousness as an emergent phenomenon arising from attention navigating a high-dimensional neural manifold $\mathcal{M} \subset \mathbb{R}^n$, where each of the 256 chakras acts as a toroidal attractor or submanifold modulating perception, emotion, and somatic awareness. By integrating manifold theory, point cloud geometry, and bioelectromagnetic fields, we reimagine the chakra system as a distributed network within the brain-body continuum. A preliminary roadmap for empirical validation using EEG, HRV, and neuroimaging is provided, offering a bridge between ancient energy models and modern science.

A central insight of this model is the identification of bidirectional signaling pathways—specifically the interplay between feedforward and feedback loops—as fundamental hallmarks of conscious processing. Recent neuroscientific studies have highlighted such recurrent circuits as essential for awareness. The 256-chakra framework aligns with this perspective, organizing its tiers of awareness around distinct bidirectional processing hubs distributed across cortical and subcortical regions. This mapping provides a plausible anatomical and functional substrate for the flow of awareness, suggesting that consciousness arises from recursive dynamics distributed across a highly differentiated, yet topologically unified, network.

2. Neural Geometry: Foundations

Neural geometry offers a mathematical lens to study brain dynamics, modeling neural activity as a continuous, high-dimensional manifold rather than a set of discrete units. This framework reveals how structured spaces underpin consciousness, with tools like manifolds and point clouds providing a geometric map of neural states.

2.1 Key Components of Neural Geometry

Neural geometry rests on several mathematical constructs that describe brain activity as a structured system:

• Manifolds: A neural manifold $\mathcal{M} \subset \mathbb{R}^n$ represents the brain’s state space, where $n$ could be the number of neurons (e.g., $10^{11}$) or dimensions of a recording (e.g., EEG channels). Despite high dimensionality, $\mathcal{M}$ often has a lower effective dimension (e.g., $\dim(\mathcal{M}) = 10$), reflecting how complex activity collapses into simpler patterns.
• Point Clouds: Neural data over time forms a point cloud $P = \{x_1, x_2, \ldots, x_T\}$, where $x_t \in \mathbb{R}^n$ captures the brain state at time $t$ (e.g., voltage across 64 EEG electrodes). Dimensionality reduction (e.g., t-SNE) extracts $\mathcal{M}$ from $P$.
• Geodesic Distances: Paths on $\mathcal{M}$ follow geodesics: $d_\mathcal{M}(x_i, x_j) = \min_{\gamma} \int_0^1 \| \gamma'(t) \| dt$, where $\gamma(t)$ is a curve connecting states $x_i$ and $x_j$. This measures the “neural distance” between experiences, unlike straight-line metrics.
• Topological Features: Persistent homology identifies loops or holes in $\mathcal{M}$ (e.g., cyclic patterns in meditative states), revealing the shape of information flow.

In consciousness, these components suggest chakras could be specific regions on $\mathcal{M}$, with geodesic paths tracing transitions—e.g., from a grounding “root chakra” state to a transcendent “crown chakra” state.

2.2 Consciousness and Geometry

Consciousness emerges from dynamic flows across $\mathcal{M}$, stabilized by attractors and modulated by attention. An attractor $\mathcal{A}$ is a stable state where trajectories converge: $$ \lim_{t \to \infty} f^t(x_0) \in \mathcal{A}, \quad x_0 \in \mathcal{B}(\mathcal{A}), $$ where $f$ represents neural dynamics (e.g., $\dot{x} = f(x)$), and $\mathcal{B}(\mathcal{A})$ is the basin of attraction. Focused attention reduces $\dim(\mathcal{M})$, creating smooth, low-entropy surfaces (e.g., during meditation), while scattered attention fragments $\mathcal{M}$, increasing entropy. Each of the 256 chakras might correspond to an attractor $\mathcal{A}_i$, with its basin tied to a unique quality—e.g., calm, creativity, or willpower—offering a finer resolution than the seven-chakra system. This geometric view casts consciousness as a journey across a structured landscape, with chakras as landmarks shaping the terrain.

3. The 256 Chakras as Geometric Fields

Sri Amit Ray’s 256-chakra system expands the traditional model into a network of 256 energetic nodes, each linked to distinct mental, emotional, or somatic states (e.g., courage, empathy, stillness). We propose these chakras are submanifolds $\mathcal{C}_i \subset \mathcal{M}$ (for $i = 1, \ldots, 256$), embedded within the global neural geometry. As attention shifts—through meditation, breath, or thought—it activates these submanifolds, tracing a path through $\mathcal{M}$. Lower chakras (e.g., survival-related) might tie to brainstem activity, while higher ones (e.g., transcendence) align with prefrontal or global synchrony. This granularity enables precise modeling of consciousness, with each $\mathcal{C}_i$ acting as a geometric “anchor” in the neural-energetic field, dynamically influencing perception and awareness.

4. Mathematical Modeling of Consciousness

4.1 Manifold Representation

We define $\mathcal{M} \subset \mathbb{R}^n$ as a smooth, differentiable manifold encompassing all consciousness states. Each chakra $\mathcal{C}_i$ is a local submanifold with a coordinate chart $\phi_i: U_i \to \mathbb{R}^d$, where $U_i$ is a neighborhood around $\mathcal{C}_i$. Energy flow between chakras is modeled by a vector field $V: \mathcal{M} \to T\mathcal{M}$, where $T\mathcal{M}$ is the tangent bundle (the set of all possible directions on $\mathcal{M}$). The Laplace-Beltrami operator $\Delta_{\mathcal{M}} u = \text{div}(\nabla u)$ measures field smoothness, with higher coherence (e.g., in meditation) yielding lower eigenvalues. This framework tracks how attention moves energy between chakras—e.g., from a “heart chakra” state of love to a “throat chakra” state of expression—quantifying transitions in consciousness.

4.2 Toroidal Field Dynamics

Each chakra’s energy is modeled as a toroidal field, reflecting its self-sustaining, resonant nature: $$ T(u, v) = \left((R + r \cos v)\cos u,\ (R + r \cos v)\sin u,\ r \sin v\right), $$ where $R$ is the major radius (distance to the torus center), $r$ is the minor radius (tube thickness), and $u, v \in [0, 2\pi]$. This doughnut shape supports feedback loops, with energy oscillating within and between chakras. In consciousness, this mirrors traditional descriptions of chakras as “spinning wheels,” with toroidal resonance tied to states like emotional balance or spiritual clarity. The global field emerges from synchrony across all 256 toroidal attractors, potentially detectable as bioelectric patterns.

4.3 Chakra Mesh

The 256 chakras form a graph $\mathcal{G} = (V, E)$, where vertices $V = \{\mathcal{C}_1, \ldots, \mathcal{C}_{256}\}$ are chakra nodes, and edges $E$ represent energetic couplings (e.g., synchrony between adjacent chakras). A point cloud $P = \{x_1, \ldots, x_{256}\}$ assigns spatial coordinates (e.g., in the body), forming a fractal mesh analyzed via geodesic distances or homology. Edges might reflect energy exchange—e.g., heart-to-throat chakra communication—while the mesh’s fractal nature captures the complexity of Ray’s system. This network integrates local toroidal fields into a dynamic, interconnected web, modeling consciousness as a unified yet distributed phenomenon.

5. Empirical Validation

To test this model, we propose three approaches:

• EEG Mapping: Extract manifolds from EEG data (e.g., 64-channel recordings) using techniques like UMAP or diffusion maps. During chakra meditation, toroidal or spiral patterns might emerge, reflecting coherent oscillations tied to specific $\mathcal{C}_i$ (e.g., alpha waves for calm states).
• HRV Analysis: Measure heart rate variability to assess autonomic balance, potentially syncing with chakra activations—e.g., heart chakra coherence as a peak in HRV power spectrum.
• Neuroimaging: Use fMRI to track blood flow, mapping real-time shifts across $\mathcal{M}$ as attention moves between chakras (e.g., from occipital to prefrontal regions).

These methods ground the 256-chakra system in data, linking geometric predictions (e.g., manifold curvature) to observable neural signatures.

6. Benefits of the Model

This framework offers several advantages for understanding and applying consciousness science:

• Holistic Integration: By merging neural geometry with the 256-chakra system, it unifies ancient energetic models with modern topology, fostering collaboration across disciplines. Chakras as submanifolds $\mathcal{C}_i$ validate traditional wisdom scientifically.
• Granular Modeling: The 256 chakras enable detailed tracking of consciousness states (e.g., from focus at $\mathcal{C}_{50}$ to intuition at $\mathcal{C}_{200}$) via geodesic paths $d_\mathcal{M}(\mathcal{C}_i, \mathcal{C}_j)$, enhancing precision in meditation or therapy.
• Testable Predictions: Hypotheses like toroidal EEG patterns ($T(u, v)$) or low-dimensional manifolds ($\dim(\mathcal{M}) \ll n$)) make the model empirically rigorous, elevating chakra practices to evidence-based status.
• Therapeutic Applications: Activating specific chakras (e.g., $\mathcal{C}_{12}$ for empathy) via biofeedback could address mental health or cognitive goals, linking neural regions to conscious states.
• Scalability: The fractal mesh $\mathcal{G} = (V, E)$ scales from individual chakras to global dynamics, adaptable to micro (neural) or macro (collective) analyses.

7. Further Research Areas

To advance this model, we propose the following directions:

• Empirical Validation: Conduct studies with EEG, HRV, and fMRI to map the 256 chakras onto $\mathcal{M}$, testing for toroidal signatures (e.g., via spectral analysis) and chakra-specific patterns (e.g., theta waves).
• Chakra Mapping: Link each $\mathcal{C}_i$ to neural regions or frequencies using source localization and graph theory on $\mathcal{G}$, creating a detailed “chakra atlas” for personalized applications.
• Attention Dynamics: Model attention shifts as trajectories in $V: \mathcal{M} \to T\mathcal{M}$ (e.g., $\dot{x} = V(x)$), validated with real-time imaging, to optimize consciousness control.
• Bioelectromagnetic Extensions: Investigate toroidal fields with Maxwell’s equations or quantum effects (e.g., entanglement), grounding chakras in physical mechanisms.
• Cross-Cultural Studies: Compare the 256-chakra system with other traditions (e.g., meridians) using topological invariants (e.g., Betti numbers), seeking universal geometric principles.

8. Significance of the 256 Chakras Over Other Systems

In this neural geometry and consciousness framework, Sri Amit Ray’s 256-chakra system holds greater significance than the traditional 7, or the expanded 114 or 144 chakra systems, due to its alignment with the model’s goals of granularity, neural complexity, and empirical rigor.

• Granularity and Resolution: Unlike the 7-chakra system’s broad regions (e.g., heart/Anahata for love), or the less symmetrical 114/144 systems, the 256 chakras offer a high-resolution map. Each $\mathcal{C}_i$ (for $i = 1, \ldots, 256$) acts as a distinct submanifold or attractor $\mathcal{A}_i$, enabling precise tracking of subtle states—e.g., compassion versus gratitude—via a dense point cloud $P = \{x_1, \ldots, x_{256}\}$ and geodesic paths $d_\mathcal{M}(\mathcal{C}_i, \mathcal{C}_j)$. This granularity matches the complexity of consciousness as a “dynamic dance” across $\mathcal{M}$.
• Neural Complexity: The brain’s $10^{11}$ neurons and fractal networks require a model with sufficient scale. The 7 chakras oversimplify this (e.g., mapping to spinal regions), while 114/144 add detail but lack the systematic coverage of 256. With 256 nodes, the chakra mesh $\mathcal{G} = (V, E)$ approximates the brain’s topological richness, potentially correlating each $\mathcal{C}_i$ with neural subsystems (e.g., prefrontal for higher chakras).
• Empirical Testability: The 256 chakras’ specificity supports testable hypotheses—e.g., a toroidal field $T(u, v)$ at 8 Hz in parietal EEG for intuition at $\mathcal{C}_{75}$—versus the vague predictions of 7 (e.g., “crown chakra” gamma waves) or the less structured 114/144 systems. This aligns with EEG and neuroimaging validation goals.
• Subtle Consciousness States: The 256 chakras encompass a wider spectrum, including transpersonal states (e.g., bliss at $\mathcal{C}_{200}$), beyond the 7-chakra survival-to-enlightenment arc or the 114/144’s partial cosmic expansions. This suits the model’s expansive view of consciousness as trajectories between attractor basins $\mathcal{B}(\mathcal{A}_i)$.
• Mathematical Advantages: With 256 as $2^8$, the system offers computational convenience for manifold learning (e.g., 256-dimensional embeddings) and fractal modeling, unlike the sparse $P = \{x_1, \ldots, x_7\}$ or irregular 114/144 counts. Tools like $\Delta_{\mathcal{M}}$ or homology thrive with this density.

Thus, the 256-chakra system is more important here because it provides a detailed, testable, and mathematically robust framework, mirroring neural complexity and enabling a comprehensive “atlas” of awareness that coarser systems cannot achieve.

9. Conclusion

This framework synthesizes neural geometry with Sri Amit Ray’s 256-chakra system, reimagining consciousness as a geometric flow across a field of toroidal attractors and submanifolds. Far from a monolithic state, awareness emerges as a dynamic journey through a structured landscape, with the 256 chakras providing a detailed atlas of mental, emotional, and spiritual states. The model’s mathematical rigor—e.g., predicting manifold shapes or toroidal signatures—makes it testable, bridging ancient intuition with modern neuroscience. Potential applications include chakra-based therapies, attention training, and spiritual exploration, inviting a new science of consciousness that honors both tradition and precision.

References

1. Ray, Amit. "The Power of 24 Healing Chakras in Your Hand." Yoga and Ayurveda Research, 3.7 (2020): 60-62. https://amitray.com/the-24-healing-chakras-in-your-hand/.
2. Ray, Amit. "Dreams Interpretation With 114 Chakras and the 72000 Nadis." Sri Amit Ray 114 Chakra System, 1.2 (2021): 48-50. https://amitray.com/common-dreams-meaning-interpretation-the-72000-nadis/.
3. Ray, Amit. "Deep Compassion: Neuroscience and the 114 Chakras." Compassionate AI, 1.3 (2021): 66-68. https://amitray.com/compassion-neuroscience-nadis-and-the-114-chakras/.
4. Ray, Amit. "Reticular Activating System for Manifestation and Visualization." Amit Ray, amitray. com, 1.5 (2021): 3-5. https://amitray.com/reticular-activating-system-for-manifestation/.
5. Ray, Amit. "How to Release Trapped Negative Emotions: By Balancing The 114 Chakras." Compassionate AI, 4.10 (2022): 90-92. https://amitray.com/how-to-release-trapped-emotions/.
6. Ray, Amit. "Neurotransmitters and Your Seven Chakras: Balancing Your Body, Mind, and Brain." Compassionate AI, 1.3 (2023): 6-8. https://amitray.com/neurotransmitters-and-the-seven-chakras/.
7. Ray, Amit. "Enthusiasm and Humbleness for Leadership: The Power of 114 Chakras." Sri Amit Ray 114 Chakra System, 2.4 (2023): 54-56. https://amitray.com/enthusiasm-and-humbleness-for-leadership-the-power-of-114-chakras/.
8. Ray, Amit. "Leadership Values and Principles: The Power of Your 114 Chakras." Sri Amit Ray 114 Chakra System, 2.5 (2023): 45-47. https://amitray.com/leadership-values-and-principles-and-your-114-chakras/.
9. Ray, Amit. "The Sama Veda Mantra Chanting: Melody and Rhythms." Yoga and Ayurveda Research, 4.12 (2023): 30-32. https://amitray.com/the-sama-veda-mantra-chanting-melody-and-rhythms/.
10. Ray, Amit. "Neural Geometry of Consciousness: Sri Amit Ray’s 256 Chakras." Compassionate AI, 2.4 (2025): 27-29. https://amitray.com/neural-geometry-of-consciousness-and-256-chakras/.
11. Ray, Amit. "Mathematical Model of Healthy Aging: Diet, Lifestyle, and Sleep." Compassionate AI, 2.5 (2025): 57-59. https://amitray.com/healthy-aging-diet-lifestyle-and-sleep/.
12. Ray, Amit. "Neuro-Attractor Consciousness Theory (NACY): Modelling AI Consciousness." Compassionate AI, 3.9 (2025): 27-29. https://amitray.com/neuro-attractor-consciousness-theory-nacy-modelling-ai-consciousness/.
1. Fang, Zepeng, et al. "Human high-order thalamic nuclei gate conscious perception through the thalamofrontal loop." Science 388.6742 (2025): eadr3675.
2. Vishne, Gal, et al. "Distinct ventral stream and prefrontal cortex representational dynamics during sustained conscious visual perception." Cell Reports 42.7 (2023).
3. Fleming, Stephen M., and Nicholas Shea. "Quality space computations for consciousness." Trends in Cognitive Sciences (2024).
4. Ma, Alex C., Ayana D. Cameron, and Martin Wiener. "Memorability shapes perceived time (and vice versa)." Nature Human Behaviour 8.7 (2024): 1296-1308.
5. Graziano, Michael SA. "A conceptual framework for consciousness." Proceedings of the National Academy of Sciences 119.18 (2022): e2116933119.
6. Dehaene, Stanislas, Hakwan Lau, and Sid Kouider. "What is consciousness, and could machines have it?." Robotics, AI, and humanity: Science, ethics, and policy (2021): 43-56.
7. Ray, Amit. The 256 Chakras: Science and Spirituality. Compassionate AI Lab, 2015.
8. Cheng, YA., Sanayei, M., Chen, X. et al. A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02149-x

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