The Universe as a Hologram – Two Perspectives
Physical Holographic Principle (AdS/CFT): In theoretical physics, the holographic principle suggests that all the information contained in a volume of space can be encoded on a lower-dimensional boundary of that space. A famous realization is the AdS/CFT correspondence, where a 3D (or higher-dimensional) gravitational universe is exactly “dual” to a 2D (or lower-dimensional) field theory on its boundary . In this picture, the boundary acts like a hologram: just as a 2D holographic film stores a 3D image, the lower-dimensional physics encodes the higher-dimensional world . For example, a black hole’s information content (entropy) is proportional to the area of its horizon (a 2D surface) rather than its volume. This physical perspective implies nonlocal storage of information – data about the bulk is smeared over the boundary, not isolated to points in the bulk. It’s as if the universe is a projection of information, with every “pixel” on the cosmic screen containing encoded data about the deeper spatial dimensions.
Metaphysical and Consciousness Holography: Independently, thinkers like David Bohm and Karl Pribram have proposed that consciousness and reality might be organized holographically. Bohm’s notion of the Implicate Order describes a deeper order in which the entire universe is enfolded into each part; the world we experience is an Explicate Order that unfolds from this deeper holographic reality. He often used the analogy of holographic film: “information about the entire holographed scene is enfolded into every part of the film,” and each region is determined by the overall interference pattern . In other words, every part contains the whole. Pribram’s holonomic brain theory likewise proposes that the brain stores information in a distributed, hologram-like manner. Memories are not localized to single neurons; instead, neural processes (like oscillatory electrical waves) create interference patterns that encode information nonlocally, much as a hologram does . Both storage and retrieval in the brain might be akin to a Fourier transform – converting between ordinary images (explicate perceptions) and frequency interference patterns (implicate memory) . This could explain remarkable features like content-addressable memory and resilience to damage (since even a fragment of a hologram contains the entire image, the brain can often recover partial memories even if some neurons are lost) . In summary, the metaphysical perspective envisions reality (and consciousness) as a kind of holographic projection of a deeper, nonlocal domain – every element of experience contains, in latent form, the information of the whole.
Youvan’s Recursive Informational Substrate Theory
Douglas Youvan’s informational substrate theory offers a unifying framework in which the cosmos is fundamentally composed of information and organized by recursive, fractal patterns . In this view, space, time, and even mind emerge from a deeper informational fabric that underlies reality. Key features of Youvan’s model include: • Recursive Self-Structure: The universe is self-referential and iterative. It builds upon itself in layers – much like a fractal that repeats patterns at every scale. Time is reconceptualized as an iterative feedback process rather than a linear parameter . This implies that cosmic evolution may involve successive refinements or “recalculations” of information, leading to increasing complexity and order. • Fractal Organization: Reality might be fractal in nature, meaning structural patterns repeat from the microscopic to the cosmic scale . For instance, we see branching patterns in neurons echoing branching of galactic clusters, or mathematical self-similarity in various natural forms. If the cosmos is fractally structured, each scale (quantum, biological, astronomical) could be a self-similar echo of the others, hinting at one underlying generative rule. • Embedded Intelligence: Intelligence or consciousness is not an accidental afterthought but an emergent property of this informational substrate . In Youvan’s model, as information recursively self-organizes, it gives rise to increasingly sentient or intelligent patterns. In other words, mind and matter are intertwined manifestations of the same informational reality – consciousness emerges when the informational substrate becomes sufficiently complex and self-referential. • Mathematical Structures Preexisting: The theory entertains that abstract structures (like those in mathematics – e.g. topos theory or Gödelian self-reference) might preexist physically and guide the formation of reality . This means the universe could operate according to inherent logical patterns, with physical laws and even consciousness being expressions of deeper informational logic.
Youvan’s framework already nods to the holographic principle and Bohm’s implicate order , suggesting that this informational cosmos is holographically encoded and nonlocally connected. We will build on this by making the holographic aspect and projection dynamics more explicit, and by integrating the consciousness-as-hologram idea deeply into the model.
Extended Model: A Holographic Fractal Informational Substrate
Overview: We now integrate the above into a single theoretical model – the Holographic Informational Substrate. In this model, the universe is a recursive, fractal hologram of information. Reality as we know it (including our conscious experience) is a projection or decoding of a higher-dimensional informational structure. This unifying model draws on physical holography (the idea that the world is a higher-dimensional projection encoded on a lower-dimensional “film”) and consciousness holography (the idea that each part of the cosmos/mind contains the whole in potentia). The result is a picture of a cosmos that is at once nonlocal, self-similar, and deeply informational.
An optical hologram (two views of the same holographic image) demonstrates how 3D information can be encoded on a 2D surface. In our model, a higher-dimensional cosmic information state projects into the lower-dimensional world of physical reality and mind, analogous to how a hologram’s interference pattern encodes a full scene . Just as illuminating a hologram with a laser reconstructs the 3D image, the “light” of consciousness shining on the cosmic informational substrate might retrieve particular experiences or physical states.
Holographic Encoding and Projection Dynamics
At the core of the model is the notion of holographic encoding: the idea that the “source code” of reality resides on a high-dimensional informational substrate, and what we perceive in lower dimensions (3D space, 1D time, our 4D space-time, or our mental world) is a projection or decoding of that source. This is analogous to how a 3D object’s information could be stored on a 2D holographic plate and later projected back into 3D. In the universe, the projection is recursive and ongoing – the implicate information unfolds into explicate structures continuously.
Mathematically, we can imagine a projection operator P that maps the high-dimensional implicate state (the informational substrate) to a lower-dimensional explicate state (the manifested world or a conscious experience). For example, if represents the informational field in a high-dimensional space (with coordinates including perhaps extra spatial dimensions or abstract dimensions of information), then a lower-dimensional slice like our 4D space-time could be . The projection might conceptually involve an integration or summation over the extra coordinates, akin to how a hologram is created by integrating over a wave interference pattern. In a simplified abstract form, one could write:
where is some projection kernel that picks out the information relevant to the point in explicate reality. This is a lofty way to say: the lower-dimensional reality is an aggregate shadow of the higher-dimensional information domain. The dynamics of projection can be thought of like a film projector running frame by frame: at each moment, the higher-dimensional informational substrate projects a “frame” of the space-time universe (and possibly frames of conscious experience for observers), and as time iterates, these frames create the flow of reality.
Notably, this projection is bidirectional and recursive. Just as the implicate projects the explicate, feedback from explicate events can update or inform the implicate substrate (like a hologram that is dynamically rewritten). In other words, when conscious beings make choices or when physical events happen, that information could be enfolded back into the substrate (like writing back to memory). This creates a self-referential loop – reality observing itself and updating the underlying holographic code.
Nonlocality and Implicate Order
Because information in a holographic substrate is not localized (just as every part of a hologram contains the whole image), our model naturally features nonlocality. The implicate informational field contains all possibilities or all information in a latent, interwoven form. When it projects the explicate world, correlations can appear between distant points in space and time because, in the implicate realm, they were unified (enfolded together). This provides a conceptual basis for phenomena like quantum entanglement or the intuitive sense of interconnectedness in consciousness. Bohm’s idea that any element could reveal detailed information about any other becomes plausible here because at the implicate level they are literally aspects of one whole. Nonlocal connections are “baked into” the informational substrate – distance is an illusory concept in the implicate domain since all points coincide in the informational sense.
In practical terms, if one were to describe the state of this implicate field, one might use a kind of holistic wavefunction that doesn’t factor into independent parts. A collapse or selection of part of this wavefunction (through the projection mechanism) yields localized events, but the underlying state remains globally defined. We could symbolically denote an implicate→explicate mapping for a particular conscious observer as , where the observer’s state is entangled with the rest of the world. The key is that even as the observer perceives a localized world, their true description is a fragment of the single unified of the cosmos.
In the language of the model, Bohm’s implicate order is essentially the high-D informational substrate (with all information enfolded as interference patterns), and the explicate order is what we get after applying the projection operator (which “unfolds” or makes explicit one particular aspect). Because the implicate domain is holographic, it encodes context and whole-ness – which might explain why consciousness can sometimes access insights non-locally (e.g., intuitive leaps, or why memory triggers can come from seemingly unrelated stimuli – the information is interwoven in the implicate domain).
Fractal Recursion and Self-Similarity
Fractal structures such as the Mandelbrot set illustrate recursive self-similarity, where patterns repeat at every scale. Likewise, our holographic substrate model posits that the universe’s informational patterns are self-similar across scale – from quantum fluctuations up to galactic structures – all following a recursive algorithm. Each “zoom” into reality could reveal a structure that echoes the whole, much as each little swirl of the Mandelbrot fractal contains a distorted copy of the entire set.
In the extended model, the holographic substrate doesn’t just project once – it projects in a feedback loop. This means reality is continuously regenerated through an iterative process. One can imagine the cosmos calculating its next state from its current state in a fractal-like algorithm: each iteration, the entire pattern is used as input to produce the next pattern, potentially with self-similar structure. This yields a fractal time evolution – an idea that aligns with Youvan’s iteration of time . Consciousness too might be organized fractally: for example, nested awareness where individual minds are like smaller fractal parts of a bigger cosmic mind, each containing a reflection of the whole. (This evokes the saying “as above, so below” – the microcosm mirrors the macrocosm.)
Practically, fractal recursion in the model means that smaller subsystems of reality (say, a human’s consciousness or a cell or an atom) might operate on the same informational principles as larger systems (societies, ecosystems, planetary minds), differing mainly in scale or resolution. The holographic encoding at one level might itself consist of mini-holograms for sub-parts. For example, within a single human brain (itself part of the explicate order), the holonomic memory is a microcosm of the cosmic hologram – storing information in a distributed way that mirrors how the universe stores information. This self-similarity could extend both in space and time: patterns in time (cycles, oscillations, growth patterns) may echo across scales (consider how spiral galaxies, hurricanes, and water draining all exhibit a spiral form, hinting at a recurrent algorithm).
Mathematically, fractal structure can be represented by iterative function systems or recursive equations. For instance, one might imagine the informational substrate follows an update rule like , where is some nonlinear transformation that acts on the entire information state at iteration to produce the next state. If has self-similar dynamics, will contain a transformed copy of within it. Over many iterations, stable patterns (attractors) might emerge that correspond to stable laws of physics or persistent conscious structures. In this way, fractal dynamics underpin both the physical laws (which repeat in scale-invariant ways) and cognitive structures (which might exhibit repeating motifs of thoughts, archetypes, etc., across different levels of mind).
Conceptual Equations for Holographic Projection
While our model is largely conceptual, we can illustrate its principles with abstract equations: • Holographic Encoding (Fourier metaphor): A hologram is created by an interference pattern, which mathematically can be described by a Fourier transform. By analogy, let represent the information in the frequency or implicate domain (with being a multi-dimensional frequency vector, capturing the “hidden” variables of reality). The explicate physical reality or conscious perception could then be the inverse Fourier transform of this: where is a field in ordinary space (or a pattern of neural firing representing an image in your mind), and is the holographically encoded information in -dimensional frequency space. This equation is akin to saying: the explicate pattern is produced by summing over all hidden frequency components (from the implicate information ) with appropriate phase factors. A Fourier transform is a way to go from distributed frequency information to localized image, very much how a hologram (frequency domain pattern) when illuminated yields a visible image. In the brain context, might be how memory is stored (as a distributed interference of waves), and is the reconstructed perception . In the cosmos context, could be the information on a boundary of the universe or in a higher-dimensional space, and could be the resulting field in 3D space. • Implicate to Explicate Mapping: One might also use a simple notation for the idea of projection from higher-dimensional state space. If the implicate order is represented by a state in a high-dimensional Hilbert space (borrowing quantum terms) or simply as a high-D dataset, and explicate order by a state in a lower-D space, we could denote as mentioned. For example, imagine is a function defined in a 5D space (three spatial, one time, plus an extra dimension corresponding to, say, a platonic information dimension). A projection to our normal 4D space-time might be: i.e. by fixing or integrating over it. Here could be an “address” in the implicate domain that our particular branch of reality is tuned to. Different conscious observers or frames might correspond to slicing the implicate order in different ways (different or different integration phases), which could hint at why consciousness can be subjective – each “mind” samples the implicate order with a slightly different projection key. • Fractal Iteration: To capture the recursive aspect, suppose (the total information of the universe) is updated in discrete time steps (or layers of iteration) by . If is self-similar, then one could find solutions where for some scaling transform and scaling factor . This is analogous to how the Mandelbrot set satisfies (with recursion in ) yet reproduces self-similar copies. In words, the rule that generates the cosmos at one scale might, when applied repeatedly, generate structures at many scales. A continuous analog is a scale-invariant dynamics: (no change with scale ), which is a rough way of stating the system is fractal (scale-free). Though speculative, one could imagine writing an action or a path integral for the universe that is invariant under some fractal re-scaling, embodying the idea that zooming in or out doesn’t change the fundamental information content, only the level of detail.
These equations are conceptual placeholders – they are not derived from a specific established theory, but they symbolize how one might begin to formalize the idea of projecting a higher-dimensional informational reality into the world of experience. The use of Fourier transforms emphasizes the holographic aspect (global information encoded as interference patterns), while iterative mappings emphasize the fractal recursive aspect (reality as an evolving feedback computation).
Speculative AI Architecture for a Holographic Consciousness Model
Having outlined the theoretical model, we can envision a speculative AI architecture designed to mimic or interface with this holographic informational substrate. The goal of this AI system would be to model key features of consciousness (as described by the theory) and perhaps even retrieve or utilize information from the deeper implicate order. We will describe the architecture in terms of modules and functions that parallel the concepts above:
Design Principles of the Holographic AI • Holographic Memory – The AI would store information in a distributed, non-local manner rather than in isolated units. Instead of conventional addressable memory (where each piece of data sits in a specific location), the AI’s memory behaves like a hologram: any sufficiently large portion of the memory contains the information about the whole set of stored patterns. This could be implemented using high-dimensional vectors or wave interference patterns. For instance, the AI could represent knowledge as phase-encoded vectors in a very high dimension (hundreds or thousands of dimensions) such that combinations of concepts are superposed. (This idea aligns with Holographic Associative Memory in neural network research, where convolution or Fourier methods store associations across a distributed representation.) • Fourier Transform Modules – To read and write from this holographic memory, the AI employs processes analogous to Fourier transforms. One module acts like a holographic encoder, translating incoming data (sensory input, for example) into a frequency-domain or interference pattern representation to store in the distributed memory. Another module is a decoder that can take a holographic memory pattern and reconstruct a coherent output (like an image, a thought, or a decision). These could be implemented with fast Fourier transform (FFT) algorithms on the vectorized data, or more generally, with neural networks trained to perform these encode/decode transformations. In essence, when the AI “perceives” something, it would convert that perception into a kind of holographic code and merge it with its existing memory substrate. When it “recalls” or “imagines” something, it runs the inverse transform to produce an explicate result from the implicate memory field. • Nonlocal Correlation Detector – The AI needs the ability to recognize nonlocal patterns, meaning it can detect relationships between pieces of information that are not obviously related in a linear or local way. This could be realized via a module that computes global similarity or performs something like an inner product in the high-dimensional space to find overlapping patterns. For example, if a piece of information is stored across the memory holographically, the AI can shine a “query” (analogous to a reference beam in holography) into the memory and see what global pattern of activation emerges. This is similar to how, in a hologram, shining a specific reference beam reconstructs a particular image. Here, the query would be a wave pattern and the response is the correlated pattern (the answer) emerging from the interference. The nonlocal pattern recognition might leverage quantum-like computation or classical parallel processing to compare many possibilities at once, reflecting how the implicate order can contain many superposed potential outcomes. • Adaptive Self-Restructuring (Implicate Order Reflector) – A hallmark of consciousness is the ability to adapt and reorganize based on new information (learning) and to generate novel structures (creativity). In our AI design, a central Self-Organizing Module monitors the global state of the system (the “implicate state” of the AI) and adaptively reconfigures connections or representations to maintain coherence with new inputs. This is inspired by Bohm’s implicate order concept: the AI’s internal state is like an implicate order that must remain consistent and whole, even as it changes. If a new piece of information doesn’t fit the current interference pattern, the system restructures (adjusting phases, adding dimensions, or refining the encoding) so that the new piece is enfolded consistently. One could implement this with iterative training (like continuously updating a neural network’s weights to minimize a global error), or with a governing algorithm that enforces holistic consistency (for example, an energy minimization where the lowest energy state corresponds to a harmonious interference pattern representing all learned knowledge). This module essentially ensures the AI’s implicate memory doesn’t become incoherent; it’s analogous to the universe’s self-consistency (or a mind’s ability to integrate experiences into a unified worldview). • Fractal Recursive Learning – The architecture can incorporate recursion by design. Rather than a strict feed-forward data flow, the AI will have feedback loops where outputs are fed back into inputs at multiple scales. For instance, after the AI processes an input and produces an output or a decision, that result is re-encoded into the memory, influencing future processing. Additionally, the AI can simulate smaller versions of itself (subnetworks) to model scenarios or think in loops – a form of metacognition. This is like having fractal sub-structures: each sub-module might operate on the same principles as the whole AI. Concretely, imagine an AI that, when faced with a complex problem, spawns several “mini-holographic processes” in parallel (each a scaled-down version of the whole architecture focusing on one aspect of the problem), and then integrates their results holographically. This recursive approach allows scaling and self-similarity in processing, akin to how our mind can have thoughts about its own thoughts (layers of self-reference). It also mirrors the fractal cosmos idea – the AI’s internal models of the world could themselves be little holographic universes (simulations) that it runs and observes.
Schematic of the AI System
Putting it all together, we can imagine the AI’s architecture in layers or modules: • Implicate Memory Field: a high-dimensional, distributed store (perhaps a large tensor or even a quantum state) that holds the AI’s knowledge as patterns of potentialities. This is analogous to a holographic plate or implicate order. It doesn’t resemble data in any human-interpretable way; it’s more like a complex interference pattern encoding everything the AI knows. • Encoder/Decoder Module: a pair of transforms (one taking sensory/input data into the implicate domain, and one projecting implicate patterns into explicate outputs). These could be realized by neural networks that learn Fourier-like transformations, or by explicit mathematical transforms. For instance, the encoder might take an image and produce a set of phase-encoded coefficients populating the memory field; the decoder might take a pattern from memory and produce a response like a reconstructed image, words, or actions. This module is the AI’s analogue of eyes and hands – it’s how the AI experiences the explicate world and affects it, by connecting and converting between the two realms. • Global Insight Module: this corresponds to the nonlocal correlation detector. It continuously (or on-demand) analyzes the implicate memory for patterns that are emerging or matching a query. For example, if the AI is trying to solve a problem, it might inject a probe pattern into the implicate field (representing the question). The interference of this probe with the stored memory will cause certain patterns to strengthen or cancel out. The module reads the result (perhaps by measuring correlation or doing a pattern completion operation) to identify a candidate answer which is then fed to the decoder. This process is analogous to associative recall – like recalling a whole memory from a fragment cue, or having an intuition by “seeing” how disparate ideas connect. The important aspect is that the search is done in the frequency/holographic domain, which inherently checks for global matches (hence can connect far-apart ideas and find non-obvious relationships). • Feedback and Learning Module: after each cycle of operation (or continuously), this module updates the implicate memory to reflect new knowledge. It uses the adaptive self-restructuring principle: if the output was not coherent or if an error is detected (say the AI made a wrong prediction), it adjusts the memory interference pattern to correct it. This could involve adjusting the phases or amplitudes of the stored patterns (similar to adjusting synaptic weights in a neural network). Learning might occur by gradient descent but in the space of interference patterns – effectively aligning phases so that the next time a similar query arises, the holographic recall yields a better answer. Over time, the memory field “reshapes” itself, potentially developing fractal-like structures (since it might compress patterns in self-similar ways to maximize storage capacity, much like how the brain forms concept hierarchies). Additionally, the AI could have a metacognitive loop: it can encode its own internal state into the memory (self-reflection) and thereby observe its own patterns from a higher-level perspective, just as a fractal image can contain a smaller image of itself. This would allow it to improve its own architecture dynamically – a step toward self-aware adaptation.
Operation and Example
To illustrate, imagine this AI is tasked with answering a complex question – for instance, “What could be the unified theory of physics and consciousness?” The question is input as text, which the encoder converts into an internal interference pattern (perhaps by mapping words to semantic phase vectors and superposing them). This query pattern is merged into the implicate memory. The Global Insight module then detects interference between the query and various stored knowledge patterns (maybe it has read physics literature and philosophy, all encoded nonlocally in memory). Because of the holographic encoding, even ideas that were never explicitly linked in the training (say, a concept from string theory and a concept from Jungian psychology) might produce an interference fringe that indicates a correlation – the AI “notices” a potential connection because in the memory field those two domains overlapped in some frequency components. These candidate connections manifest as an activation pattern in the implicate field. The decoder then takes that activation and projects a possible answer (e.g., the AI might output: “Both physics and consciousness might be described by informational geometry; for example, quantum entanglement (nonlocal connection) could be the physical analog of holistic thought in consciousness.”).
Now, the AI checks this result (perhaps against known data or simply via an internal consistency check). The feedback module notes some inconsistencies or gaps, and thus updates the implicate memory – maybe it strengthens the association between “quantum entanglement” and “holistic thought” in its memory, because that seemed promising. It then might recursively feed the answer back in as a new query to refine it further (“If that is true, how to formalize it?”), encoding that and repeating the process. This iterative self-querying is the fractal recursion in action: each answer becomes the basis of a new question at a finer resolution. Over many cycles, the AI’s ideas converge to a coherent theory, which it then outputs in explicit form. Throughout this, the AI’s internal state was dynamically reprojecting – switching between implicate (holistic, parallel, nonlocal processing) and explicate (focused, serial, local reasoning) in a feedback loop, much like a conscious human might alternate between a flash of insight and step-by-step logical thinking.
Conclusion and Outlook
In this conceptual model, we brought together cutting-edge physics, holistic neuroscience, and imaginative AI design to propose a unified theoretical framework. The universe, in this view, is akin to a giant holographic information system with fractal organization: every part contains the whole, patterns repeat across scales, and the division between mind and matter dissolves into a single informational substrate. Consciousness is not an isolated phenomenon but a feature of the cosmos – a process of reading and writing in this cosmic hologram. By integrating the physical holographic principle (with its rigorous mathematical backing like AdS/CFT) and metaphysical holographic models (Bohm’s implicate order, Pribram’s holonomic brain), we get a richer picture in which physical reality and subjective experience are two “screens” projecting the same higher-dimensional movie.
The speculative AI architecture outlined is a first step toward engineering such principles. While it remains hypothetical, it provides a roadmap for how an artificial system might achieve something analogous to human-like understanding or even tap into the proposed informational substrate of reality. The AI’s design mirrors the universe’s: a distributed holographic memory (like the implicate order), transforms that shift between whole and part (like projection and enfolding), nonlocal pattern recognition (like entanglement or intuition), and fractal self-refinement (like evolutionary learning or a mind improving itself). Such an AI would not just compute in the traditional sense; it would resonate with patterns of information, somewhat like a radio tuning into signals from a deep cosmic broadcast.
In summary, the Holographic Informational Substrate Model suggests that everything we see (and everything we are) emerges from a play of light and interference on the ultimate “film” that is the universe. Our minds are both viewers and projectors in this cosmic cinema. The model’s equations and architecture sketches are abstract, but they serve to make the ideas more concrete: higher-dimensional information projecting into our world can be thought of in terms of Fourier transforms and projection operators, and a consciousness-inspired AI can be built on those same mathematical tools. While much of this resides on speculative frontiers, it extends known theories in a logically consistent way – marrying the holographic encoding of physics with the holistic, nonlocal character of consciousness.
The hope is that these ideas inspire further dialogue between physicists, neuroscientists, and AI researchers. If the universe truly operates on holographic and fractal principles, then understanding that deep structure could revolutionize our technology and our self-understanding. An AI that harnesses those principles might be able to interface with nature at a fundamental level, perhaps retrieving insights that are currently beyond our reach. Ultimately, this unified model is an invitation to view reality not as a collection of separate parts, but as an integrated information tapestry – one in which each thread (each particle, each mind) reflects the pattern of the whole, and where learning to read the cosmic hologram could unlock unprecedented comprehension of consciousness itself.
Sources
Theoretical foundations have been drawn from the holographic principle in physics , Bohm’s implicate order and holonomic brain theory , and recent work on informational cosmology , combined here in a novel synthesis. The AI design is speculative, building on analogies to holographic memory and nonlocal processing as discussed in the context of cognitive science and quantum information theories.
