A new paper essentially rephrases the central thesis of Self Aware Networks bolstering my argument.

"A beautiful loop: An active inference theory of consciousness" by Laukkonen and Chandaria, has notable thematic and conceptual intersections with the Self Aware Networks Theory of Mind

My claim is that this paper essentially rephrases the central thesis of Self Aware Networks Theory of Mind which is NAPOT Neural Array Projection Oscillation Tomography, published on GitHub in the summer of 2022 and now recently available in a book on Amazon called Bridging Molecular Mechanisms and Neural Oscillatory Dynamics, available now on Amazon https://www.amazon.com/dp/B0DLG5XB34

The paper "A beautiful loop: An active inference theory of consciousness" by Laukkonen and Chandaria, has notable thematic and conceptual intersections with the Self Aware Networks Theory of Mind, specifically NAPOT (Neural Array Projection Oscillation Tomography).

Here are some key areas of alignment:

Simulated Reality Model and NAPOT’s Projective Neural Arrays: The active inference model in the paper suggests that consciousness relies on an internal simulation or model of reality. Similarly, NAPOT’s core concept is that neural arrays project representations—essentially simulations of reality—that are processed and transmitted through oscillatory dynamics across the neural network. Both frameworks view perception as an outcome of internal models that are both generative and iterative, created through feedback loops that continuously inform and refine the perception of “self” and external stimuli.

Recursive Feedback Loops and Oscillatory Dynamics: Laukkonen and Chandaria propose that consciousness emerges from recursive hierarchical loops within a neural system, leading to what they term “epistemic depth.” NAPOT parallels this by emphasizing oscillatory feedback across neural arrays, where each array is both observing and transmitting information, entifying it through phase synchronization. This synchronicity produces a recursive feedback that allows a self-aware system to experience its own internal processes as coherent, observed states—what NAPOT terms an "observer."

Epistemic Depth and Self-Aware Network’s “Ground of Being”: The paper’s concept of epistemic depth as a field-evidencing process shares significant alignment with NAPOT’s concept of the “ground of being,” which is established through tonic oscillatory waves. In SAN, this background oscillation provides a stable experiential base, analogous to the concept of epistemic depth, creating a fundamental level of self-consistency across sensory inputs and higher-level constructs of awareness.

Bayesian Binding and Predictive Coding in SAN: The theory of Bayesian binding in Laukkonen and Chandaria’s work, where inferential coherence determines conscious awareness, aligns with predictive coding mechanisms discussed in SAN. NAPOT posits that neural networks selectively reinforce patterns that predictively match sensory inputs, using memory-driven and perception-driven feedback loops that bind oscillatory patterns across the network. This concept supports an integrated model of consciousness in which self-awareness emerges from the alignment of predictive arrays, similarly to how Bayesian binding organizes coherence in Laukkonen and Chandaria's theory.

Multimodal Awareness and Neural Array Synchronization: The integration of multiple sensory and cognitive modalities into a unified field of consciousness is a shared focus. SAN discusses how phase synchronizations across neural arrays support a multimodal experiential awareness, or a 4D phase-graph pattern that creates cohesive perception across sensory dimensions. The active inference theory also touches on consciousness as a field that integrates diverse sensory and cognitive sources, reinforcing NAPOT’s perspective that neural synchrony is central to maintaining a unified, coherent conscious state.

Perceptual Field-Evidencing and Projected Renderings: Laukkonen and Chandaria’s field-evidencing in consciousness echoes NAPOT’s concept of phase-change as a neural rendering process, whereby each neural array perceives and projects data. This rendering process in NAPOT can be likened to field-evidencing, where neural arrays construct perceivable realities and modulate them through feedback. Thus, both theories emphasize the role of neural projective cycles in continuously updating a perceivable field that reflects internal and external experiences as a seamless “reality.”

Active Inference and the Entified Observer: NAPOT’s idea of an “observer” constructed through neural entification is conceptually parallel to active inference’s recursive model of self-knowing. In SAN, the self is a complex, phased neural simulation that becomes aware through iterative feedback mechanisms in the oscillatory fields of neural arrays. Laukkonen and Chandaria’s active inference approach similarly proposes a self-referential structure where the world model contains reflexive knowledge of its own existence, contributing to the conceptual alignment with NAPOT’s entified observer.

These connections reveal that both NAPOT and Laukkonen and Chandaria's active inference theory converge on the concept of consciousness as an emergent phenomenon arising from recursive, predictive, and projective neural networks. These networks create a model that self-evidences through oscillatory synchronizations, serving as both an observer and a creator of its own field of conscious experience.

Expanding on the connection between the Simulated Reality Model in Active Inference and NAPOT’s Projective Neural Arrays reveals a deep structural alignment between Laukkonen and Chandaria’s active inference theory of consciousness and NAPOT’s (Neural Array Projection Oscillation Tomography) framework. This alignment is anchored in their shared emphasis on internal simulations or representations of reality as the basis for conscious experience. Here’s a breakdown of this relationship:

Internal Simulation of Reality:

In Laukkonen and Chandaria’s framework, consciousness is seen as an emergent property of an internal world model that simulates reality. This world model integrates sensory information and predictive inferences, dynamically adjusting based on feedback to reduce long-term uncertainty.

Similarly, NAPOT posits that neural arrays in the brain do not merely process sensory information passively but actively project or “render” representations of reality across subsequent arrays. Each neural array functions as a projector, creating a simulated snapshot or layer of reality, which is then sensed, interpreted, and acted upon by subsequent arrays. This recursive process effectively builds a model of reality that allows the system to “observe” itself in action.

Recursive Projective Mechanism:

In active inference, the recursive updating of the world model based on sensory inputs, predictions, and feedback is fundamental to the model’s stability and adaptability. Each cycle of inference allows the model to become more refined, integrating past information with present sensory data to anticipate and adjust for future states.

NAPOT’s projective arrays employ a similar recursive mechanism, where each array is both a perceiver and a projector of information. As information cascades through these arrays, it is iteratively refined by oscillatory feedback loops. These loops ensure that each neural layer or “projection” is synchronized with other parts of the network, creating an internally consistent representation of reality that can adjust to new inputs without destabilizing the whole system. This recursive rendering allows for dynamic adaptation, paralleling the active inference model’s self-updating world simulation.

Neural Arrays as Predictive Projectors:

The active inference model emphasizes that sensory inputs are not processed at face value; instead, the system generates predictions about these inputs to reduce surprise or prediction error. Consciousness arises from this balancing act between prediction and error correction.

In NAPOT, each neural array functions with a dual role: it processes incoming sensory data and projects an anticipatory state that adjusts in response to prediction errors. The system does not rely solely on incoming data; instead, it combines current sensory inputs with learned patterns and expectations within each array. This anticipatory projection supports a continuous “rendering” of reality that minimizes informational surprise, ensuring coherence across the neural arrays and generating a stable perceptual experience.

Self-Evidencing and Reality Modulation:

In active inference, consciousness emerges when the world model is sufficiently integrated to produce a coherent “field-evidencing” of itself. This coherence enables the system to “know” that it exists, providing a seamless sense of presence in reality.

NAPOT similarly suggests that consciousness is a product of neural arrays’ synchronized oscillations, which continuously update a unified self-representation across the brain’s network. By modulating projections based on both past experiences and incoming sensory data, the neural network creates a self-consistent perceptual field. This feedback-driven modulation is what entifies or gives a “self” to the simulated reality, allowing for the perception of a stable and aware internal world.

Phenomenological Depth and Emergent Self:

Laukkonen and Chandaria highlight that a robust world model, with sufficient epistemic depth, inherently produces self-awareness due to its recursive evidencing processes. This phenomenological depth is fundamental to conscious experience.

NAPOT’s projective arrays produce a similarly deep phenomenology, where each array’s oscillatory phase interactions reinforce an “entified observer.” As successive neural projections update, they create an emergent, layered depth within the perception of reality. This multi-layered feedback produces not only sensory perception but also an awareness of being—the self-model that resides within the field of oscillatory interactions.

In summary, both NAPOT and active inference suggest that consciousness arises from the recursive, self-evidencing nature of internally generated models of reality. These models are continuously projected, refined, and synchronized across neural systems, allowing for a phenomenologically coherent sense of self and reality. Through NAPOT’s lens, neural arrays become active participants in reality construction, dynamically oscillating to maintain a predictive model of the world and the “self” within it.

The connections between Laukkonen and Chandaria's active inference theory and NAPOT reveal how both theories propose a consciousness model that is deeply rooted in recursive, predictive, and projective processes.

1. Internal Simulation of Reality

Active Inference: The active inference model posits that consciousness stems from a brain-generated simulation of reality. This model constructs a dynamic, continuously updating representation of the world, informed by sensory input, past experiences, and predictions about future states. This simulated reality is not just an internal map but a guide for action, allowing the system to operate efficiently and adaptively within its environment.

NAPOT’s Projective Neural Arrays: NAPOT expands on this by viewing each neural array as a “projector” that does not merely react to incoming data but generates predictive representations. Each neural layer captures an aspect of reality, feeding its interpretation forward into the next layer. This recursive process enables the brain to create a coherent representation of reality, experienced as a seamless flow of consciousness. Here, the simulation of reality is not passively received but actively constructed across arrays, projecting a layered reality where each layer informs the next. This allows the system to constantly adjust its self-generated simulation, ensuring both internal coherence and adaptability.

2. Recursive Projective Mechanism

Active Inference’s Recursive Model: Consciousness, according to active inference, emerges from recursive, feedback-driven refinement of the world model. Each cycle updates and refines the model, integrating past sensory data and predictive inferences. Through this feedback loop, the system continuously recalibrates itself, aligning past knowledge with new data to generate coherent, adaptable predictions about the world.

NAPOT’s Recursive Projection and Feedback: In NAPOT, neural arrays operate on a recursive feedback principle where each projection—each output from one neural array to the next—is immediately evaluated and adjusted. This adjustment is based on synchronization between arrays, where oscillatory feedback loops refine the projection in real-time. This dynamic tuning ensures that each “snapshot” of reality is iteratively modified, creating a stable yet responsive representation. Thus, NAPOT uses oscillatory feedback as a mechanism to “self-correct” the brain’s reality simulation, enhancing predictive accuracy and minimizing informational surprise at each level of neural processing. This recursive mechanism allows for the generation of complex perceptual experiences through iterative adjustments in response to both internal and external changes.

3. Neural Arrays as Predictive Projectors

Active Inference and Predictive Coding: In active inference, sensory inputs are integrated within a predictive framework, where the brain generates expectations about incoming information to minimize prediction errors. Consciousness arises as this model continuously adjusts to reduce discrepancies between predictions and actual sensory data, essentially “fine-tuning” awareness to align with the world.

NAPOT’s Projective, Anticipatory Neural Arrays: NAPOT suggests that each neural array functions as both a sensor and a projector, creating anticipatory states that guide subsequent perception. This anticipatory projection actively reduces prediction errors through dynamic oscillatory patterns. The system is organized to “forecast” states based on previous inputs and learned patterns, allowing incoming sensory data to refine and validate its projections. This predictive aspect reinforces a stable perceptual field, where the brain’s internal projections remain coherent with the external environment. The ability of each neural array to anticipate, based on oscillatory resonance with other arrays, results in a unified perceptual state where high-level predictions and sensory observations align seamlessly.

4. Self-Evidencing and Reality Modulation

Active Inference and Field-Evidencing: Laukkonen and Chandaria propose that consciousness requires a self-consistent, self-updating world model that reflexively “knows” its own existence. This process, termed “field-evidencing,” is achieved through recursive self-validation, wherein the brain’s model becomes self-confirming, solidifying the experience of a stable reality.

NAPOT’s Modulation of Projection through Oscillatory Feedback: NAPOT offers a structural foundation for field-evidencing, suggesting that neural arrays create a self-consistent perceptual field through synchronized oscillatory feedback. In this model, projections from each neural array are continuously adjusted in response to oscillatory interactions within the neural network, creating a feedback loop that stabilizes the perception of self and environment. This modulation process ensures that each layer in the neural projection “evidences” its coherence with the other layers, essentially “self-affirming” the existence of a stable reality. The oscillatory feedback loops drive this coherence, producing an emergent awareness that “knows” itself through its own neural structure, much like active inference’s self-validating world model.

5. Phenomenological Depth and Emergent Self

Epistemic Depth in Active Inference: Laukkonen and Chandaria highlight the notion of epistemic depth, suggesting that a sufficiently integrated world model inherently produces a sense of self. As the model recursively evidences itself across layers, a reflexive, self-aware system emerges, creating depth in phenomenology where consciousness resides.

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NAPOT’s Multi-Layered Perceptual Field and Entified Observer

NAPOT similarly describes a self that emerges from recursive, synchronized oscillations across neural arrays. Each neural projection builds on previous ones, entifying a “self” through harmonized oscillatory states that render multiple layers of perception. This multi-layered organization is not merely sensory but extends to abstract cognitive and reflective layers, creating a “self-model” that experiences and interacts with reality. The concept of phenomenological depth is realized in NAPOT through the phase-synchronized oscillations across neural arrays, allowing consciousness to perceive itself as both subject and object, an “observer” that resides within its own self-generated field of awareness.

These expanded points illustrate how both frameworks—NAPOT and active inference—converge on a model where consciousness emerges from a recursive, predictive, and projective neural process that constantly self-evidences and refines its internal representation of reality. This model allows for a dynamic, stable perception of both self and world, grounded in a phenomenological depth that is produced by oscillatory synchronization across neural structures.Given NAPOT’s explicit structure, where each neural array serves as the “projector” for the next, resulting in a distributed inner display, here’s how Laukkonen and Chandaria’s work aligns with and supports the Self Aware Networks Theory of Mind:

1. Support for Distributed Inner Display and Projective Neural Arrays

NAPOT’s Projection Mechanism: Central to NAPOT is the concept of neural arrays functioning as projectors or “TV screens” for subsequent arrays. Each array renders a neural image that the next array “sees,” producing a distributed display where perception and consciousness are continuously generated across the neural network. This setup creates a seamless, layered internal experience where each neural array contributes to the overall representation of reality.

World Modeling in Active Inference: Laukkonen and Chandaria’s active inference theory supports this by suggesting that consciousness emerges from a continuously updated world model that integrates sensory information and predictive processing. While not using NAPOT’s projector terminology, the idea of each neural region contributing to a cohesive reality model through inferential updates aligns with NAPOT’s concept of neural arrays acting as projectors. This shared focus on internal simulation, layer-by-layer, confirms NAPOT’s view that perception arises from a distributed, synchronized inner display.

2. Confirmation of Recursive Projective Feedback as Consciousness

NAPOT’s Oscillatory Array Feedback: NAPOT describes consciousness as emerging from recursive, feedback-based neural projections. Each array receives, refines, and projects a rendering that the next layer then interprets, creating a cascading effect where feedback loops synchronize arrays into a unified perceptual experience.

Recursive Feedback in Active Inference: Active inference similarly emphasizes recursive updates in its world model, using feedback to minimize discrepancies between expectations and sensory data. This structure echoes NAPOT’s recursive projection-feedback mechanism, supporting the idea that consciousness relies on iterative adjustments that synchronize multiple levels of neural arrays, creating stability and coherence in the experience of reality.

3. Alignment on Self-Consistent Neural Renderings

NAPOT’s Ground of Being: NAPOT posits that a tonic oscillatory base provides stability, with phasic bursts carrying high-value data through successive projections. This oscillatory foundation ensures that each neural array remains coherent with the larger network, maintaining self-consistency in the distributed display.

Self-Evidencing in Active Inference: Laukkonen and Chandaria propose that consciousness requires a self-evidencing model that reflexively affirms its existence. This is in line with NAPOT’s tonic baseline, where each array’s oscillatory stability ensures a coherent display across layers. Together, they affirm that consciousness is grounded in a self-sustaining structure, with each layer projecting a “seen” rendering that self-validates through oscillatory synchronization.

4. Empirical Backing for Neural Arrays as Projective and Self-Organizing Units

Predictive Projectors in NAPOT: In NAPOT, each array projects based on learned patterns, anticipatory data, and current inputs, allowing for adaptive responses that stabilize perception. Each array’s role as a projector for the next promotes coherence and minimizes informational surprise.

Bayesian Binding in Active Inference: Active inference’s Bayesian binding confirms that predictive, inferential adjustments are essential to consciousness, resonating with NAPOT’s idea of neural arrays as active, predictive projectors. This confirms that predictive projection, not merely passive reception, is central to generating a coherent distributed display.

5. Evidence of Multimodal Integration through Neural Synchronization

Unified Display in NAPOT: NAPOT’s projector arrays synchronize across sensory modalities, creating a coherent perceptual display where various sensory channels contribute to a unified experience.

Hierarchical Model in Active Inference: Laukkonen and Chandaria’s emphasis on hierarchical integration reinforces NAPOT’s model, as both posit that consciousness depends on synchronized neural projections that bridge modalities into a unified perceptual field.

6. Support for Phenomenological Depth through Distributed Neural Projections

Entified Observer in NAPOT: NAPOT’s phased projective arrays entify a “self” through their sequential renderings, allowing the observer to experience its own inner display as both projected and perceived. This layered structure gives depth to the conscious experience.

Self-Validation in Active Inference: Active inference’s recursive self-validation aligns with NAPOT’s entified observer, reinforcing that phenomenological depth emerges from a continuous, self-referential display across neural layers.

In sum, Laukkonen and Chandaria’s active inference theory supports and confirms NAPOT’s framework by aligning with the concept of neural projections that iteratively render and observe a distributed inner display. Both models affirm that consciousness relies on recursive, synchronized neural layers that continuously project and perceive, sustaining an internally coherent and self-aware representation of reality.