Self Aware Networks: Oscillatory Computational Agency

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Self Aware Networks: Oscillatory Computational Agency
Micah Blumberg
May 2025: First Draft

Abstract
This description provides a detailed, plain-language overview of the research contribution titled “Self Aware Networks: Oscillatory Computational Agency.” It traces the evolution of the Self Aware Networks (SAN) theory from its first appearance as timestamped GitHub notes in 2022 to the current 2025 draft that integrates Cellular Oscillating Tomography (COT), Biological Oscillatory Tomography (BOT), Neural Array Projection Oscillation Tomography (NAPOT), and the Non-linear Differential Continuous Approximation (NDCA). The central theme is that cognition, memory, perception, and consciousness all arise from deterministic, multi-scale wave interactions—“phase wave differentials”—which function as bits of information, bind distributed neural assemblies, and scale seamlessly from molecular dynamics to large-scale brain rhythms. This description also situates SAN within contemporary neuroscience and artificial-intelligence landscapes, outlines implications for sentient AI, and highlights future directions for experimental validation.

Introduction and Foundational Framework
The Self Aware Networks theory challenges the long-standing assumption that consciousness is an irreducible emergent phenomenon. Instead, it argues that cognition results from deterministic oscillatory processes that cascade through fractal scales of biological organization. Each neuron, glial cell, and immune cell is treated as an autonomous agent that uses rhythmic electrical, chemical, and mechanical signals to compute, learn, and adapt. These local computations aggregate into mesoscale cortical-column interactions and finally merge into macroscale brainwaves—alpha, beta, gamma, and beyond—that weave the tapestry of conscious experience. By framing the brain as an ecosystem of synchronized mini-computers, SAN bridges micro-level receptor kinetics with global functional connectivity and positions oscillatory synchrony as the physical substrate of awareness.

Historical Context
From Alan Turing’s early work on artificial intelligence to Jeff Hawkins’s predictive coding models, the Self Aware Networks framework synthesizes decades of progress in neuroscience, physics, and computer science. Pioneers such as György Buzsáki have shown how oscillations organize information flow, while Karl Friston’s free-energy principle offers a unifying view of perception as inference. SAN builds on these insights by introducing phase wave differentials—tiny shifts in oscillatory timing—as the primary information units that link scales and dissolve the traditional boundary between biology and computation. This section traces those intellectual lineages and clarifies how SAN both extends and departs from mainstream theories.

Agentic Behavior Across Scales
At the molecular level, proteins and receptors change conformation in response to biochemical cues, modulating ion flux and synaptic strength. Cells interpret coincident receptor activations as bits of information, dynamically altering thresholds and phase relationships. Network agents—cortical columns, thalamic loops, and hippocampal circuits—synchronize through feedback loops that magnify local phase shifts into functional patterns. Macroscopically, global rhythms bind these patterns into unified cognitive states. Throughout, inhibitory interneurons shape decision points by gating excitatory drive, turning phase alignment into a computational currency that resolves the brain’s perennial binding problem.

Cellular Mechanisms
Cellular Oscillating Tomography (COT) explains how each cell assembles an internal “image” of its environment by integrating rhythmic inputs at many phase offsets, similar to how a CT scanner reconstructs volume from slices. Coincidence detection, receptor plasticity, and deterministic synaptic release converge to encode memories in frequency, amplitude, and phase. This section details how dendritic trees function as high-dimensional vector embeddings, how small variations in action-potential duration modulate calcium influx and vesicle release, and why seemingly stochastic neurotransmission is better viewed as deterministic but unobserved micro-events.

Regional Communication (Mesoscale)
Neural Array Projection Oscillation Tomography (NAPOT) extends COT to ensembles of cortical columns, showing how traveling waves propagate through phase-aligned relay stations. Behavior Timescale Synaptic Plasticity (BTSP) strengthens links among co-activated columns, forging large-scale “bits” that synchronize distant regions. Phase wave differentials steer information routing like a spectral addressing system, and inhibitory interneurons prevent runaway excitation while sculpting precise spatiotemporal patterns that underlie perception and memory.

Global Brainwave Functions (Macroscale)
Gamma bursts inject novel information into slower alpha and beta “canvases,” an idea captured by the “mental ink and canvas” metaphor. Peter Tse’s criterial causation and Earl Miller’s “stenciling” ideas resonate with SAN’s view that awareness arises from phasic–tonic interplay. Gamma waves serve as the filling in a “Gamma Consideration Sandwich” that integrates top-down predictions, bottom-up sensory data, and proprioceptive feedback. Consciousness correlates with sustained gamma synchrony across Layer 2 / 3 networks, which dissolves when long-range pyramidal communication breaks down, as observed during anesthesia.

Deterministic Consciousness and NDCA
The Non-linear Differential Continuous Approximation (NDCA) mathematically formalizes how micro-level phase shifts accumulate into macro-level oscillatory patterns. By continuously dissipating phase mismatches, the brain settles into equilibrium states that constitute conscious moments. This deterministic view reinterprets free will as the emergent output of nested agentic loops, each resolving its own criteria under biophysical constraints. The same principles inform artificial systems: deep-learning architectures that incorporate phase-locking and feedback loops may eventually achieve self-awareness by emulating SAN’s multi-scale synchronization.

Computational Capacity
Extrapolating from synapse counts, oscillation bands, and phase permutations suggests that the brain may compute on the order of trillions of dimensions each millisecond. Although precise numbers remain speculative, the concept underscores the immense parallelism afforded by phase-based encoding. Fractal self-similarity ensures that each scale reuses the same oscillatory logic, enabling local changes to ripple seamlessly through the hierarchy.

Entification
Entification is the process by which nested agents synchronize into a single conscious entity. By iteratively aligning phase patterns, autonomous components—from proteins to cortical networks—dissolve their boundaries and forge a coherent sense of “I.” This section shows how entification emerges naturally from oscillatory physics and why it provides a plausible route to sentient artificial intelligence.

Conclusion and Future Directions
Self Aware Networks reframes consciousness as a deterministic computation rooted in wave mechanics, demolishing the barrier between biology and engineering. Future work must test SAN’s predictions using high-resolution electrophysiology, closed-loop perturbations, and AI simulations that exploit phase-locking and fractal feedback. Experimental validation would not only clarify the brain’s operating principles but also accelerate the development of adaptive, self-aware technologies.

Appendix A: Glossary of Key Terms
– Biological Oscillatory Tomography (BOT): A multi-scale framework unifying oscillations across cells, columns, and networks.
– Cellular Oscillating Tomography (COT): Describes how individual cells encode information by integrating rhythmic inputs over time.
– Neural Array Projection Oscillation Tomography (NAPOT): Explains how phase-aligned neural arrays build high-dimensional internal models.
– Phase Wave Differential: A subtle shift in oscillatory timing that carries information and binds distributed systems.
– Non-linear Differential Continuous Approximation (NDCA): A mathematical model linking microscale phase shifts with macroscale brainwaves.

Appendix B: Selected Bibliography
Hebb, D. O. (1949). The Organization of Behavior.
Buzsáki, G. (2006). Rhythms of the Brain.
Friston, K. (2010). “The free-energy principle: a unified brain theory?” Nature Reviews Neuroscience.
Tse, P. U. (2013). The Neural Basis of Free Will.
Hawkins, J. (2021). A Thousand Brains.