The Input-Output Bandwidth Asymmetry of the Human Brain: A Computational Paradigm Based on Self-Organization, Selective Verification, and Self-Proof-of-Work

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Title: The Input-Output Bandwidth Asymmetry of the Human Brain: A Computational Paradigm Based on Self-Organization, Selective Verification, and Self-Proof-of-Work

Abstract:

The human brain exhibits a remarkable disparity between its input and output bandwidths. While sensory input, primarily through vision, is estimated to be in the 10 megabits per second range, conscious output, such as speech or motor actions, is limited to a few bits per second. This paper proposes a computational paradigm to explain this asymmetry, grounded in the principles of self-organization, selective verification (specifically, the Principle of Photon Selection), and continuous self-proof-of-work. These principles, inspired by the Ground State Information Self-Organizing Model (GSISOM) and an “informational universe” perspective, suggest that the brain’s input bandwidth is constrained by the physical limitations of sensory channels, while the output bandwidth is limited by the computational cost of ensuring the validity, consistency, and effectiveness of expressed information. The brain is viewed as a hierarchical, self-organizing information processing system where information at each level must undergo “self-proof-of-work” to be integrated and ultimately expressed. This paper explores the implications of this paradigm for understanding brain function and designing artificial intelligence systems.

Keywords: Human Brain, Input-Output Bandwidth, Self-Organization, Photon Selection, Self-Proof-of-Work, Information Theory, Neuroscience, Computational Paradigm, GSISOM, Informational Universe, Active Inference.

1. Introduction

The human brain is a complex information processing system capable of remarkable feats of perception, cognition, and action. However, a striking asymmetry exists between the brain’s capacity to receive information (input bandwidth) and its capacity to express information (output bandwidth). Estimates suggest that the human brain receives sensory input, predominantly visual, at a rate of approximately 10 Mbps. In stark contrast, conscious output, such as speech or motor actions, is limited to a bandwidth of around 10 bps.

This vast difference raises fundamental questions about the nature of information processing in the brain. Why is there such a discrepancy? What are the computational principles that govern this asymmetry? And what can this tell us about the design of artificial intelligence systems?

This paper proposes a computational paradigm to address these questions, based on three core principles:

  • Self-Organization: The brain’s structure and function emerge from the dynamic interaction of its components, without external programming or central control.

  • Selective Verification (Photon Selection): The brain’s perception of the world is fundamentally constrained by the Principle of Photon Selection, which states that only information compatible with the properties of photons can be directly perceived. This is generalized to a principle of selective verification at multiple levels of processing.

  • Continuous Self-Proof-of-Work: Information within the brain must continuously “prove” its validity and consistency with respect to the brain’s internal model and the constraints of the external world.

These principles are inspired by the Ground State Information Self-Organizing Model (GSISOM), which posits a universe fundamentally composed of information, originating from a simple “ground state” and evolving through self-organization.

2. Theoretical Foundations

Our computational paradigm rests upon the following key concepts:

  • Informational Universe: Matter, energy, space, and time are emergent properties of underlying information processing.

  • GSISOM: The Ground State Information Self-Organizing Model proposes a universe originating from “ground state information” (An(P0=0)) that self-organizes within a “virtual space” to produce the “physical space.”

  • Principle of Photon Selection: Our perception is limited to information that can interact with photons, thus possessing attributes that “confirm” the photon’s existence.

  • Self-Proof-of-Work: Information within the physical space (and, by extension, within the brain) must continuously validate its existence and properties by adhering to fundamental laws and constraints. This is analogous to a continuous “proof-of-work” algorithm.

  • Minimum Action Principle: The dynamics of information flow and processing are governed by a principle analogous to the Principle of Least Action, favoring “optimal” or “efficient” pathways.

  • Hierarchical Information Processing: The brain processes information in a hierarchical manner, with increasing levels of abstraction and integration.

  • Active Inference: The brain can be modeled as performing active inference, constantly minimizing a free energy functional (or prediction error).

3. The Brain as a Self-Organizing Information Processing System

The human brain can be viewed as a complex, self-organizing information processing system:

  • Neural Networks: Nearly 100 Billions of neurons interconnected via synapses form a vast and dynamic network.

  • Self-Organized Learning: The network’s structure and function are shaped by experience and internal dynamics, through processes like synaptic plasticity and Hebbian learning.

  • Emergent Properties: Higher-level cognitive functions (perception, attention, memory, language, consciousness) emerge from the self-organized activity of the neural network.

4. Input Bandwidth: The Flood of Sensory Data

The brain’s input bandwidth is determined by:

  • Sensory Channels: Our senses (vision, hearing, touch, etc.) provide the primary channels for information input.

  • Physical Limits: The capacity of these channels is ultimately limited by physical factors, such as the number and sensitivity of sensory receptors.

  • Parallel Processing: The brain processes sensory information in a highly parallel manner, allowing it to handle a large volume of data simultaneously.

  • Photon Selection at the Periphery: The Principle of Photon Selection operates at the very periphery of our sensory systems. For instance, our eyes are only sensitive to a specific range of electromagnetic radiation (visible light).

5. Output Bandwidth: The Bottleneck of Self-Proof-of-Work

The brain’s output bandwidth is significantly lower than its input bandwidth. We propose that this is due to:

  • Hierarchical Processing and Information Compression: The brain processes raw sensory input through multiple hierarchical layers, extracting features, building representations, and compressing information.

  • Self-Proof-of-Work as a Constraint: Before information can be expressed as output (speech, action, etc.), it must undergo a rigorous process of “Self-Proof-of-Work.” This involves:

    • Internal Consistency Checks: Ensuring the information is consistent with the brain’s internal model of the world.

    • Predictive Coding: Generating predictions about the consequences of expressing the information and comparing these predictions to expected outcomes.

    • Error Minimization: Minimizing discrepancies between predicted and actual outcomes (related to active inference and the free energy principle).

    • Action Selection: Choosing actions that are likely to achieve desired goals, based on the internal model.

    • Sequential Nature: The process of forming intentions, planning actions, and executing them involves a sequence of computational steps, limiting the rate of information output.

    • Energy Efficiency: The “Self-Proof-of-Work” process is likely optimized for energy efficiency, favoring slower, more deliberate outputs.

  • Homogeneous Information Verification: Output, being a higher-level, integrated form of information, requires a stringent verification process to ensure coherence and relevance. This involves checking for consistency across different brain regions and modalities, acting as a bottleneck.

6. The Role of Photon Selection and Least Action

  • Photon Selection: Constrains the input to the brain, defining the “raw material” for information processing. It acts as an initial filter, allowing only photon-compatible information to enter the system.

  • Least Action: Governs the internal processing of information, shaping the dynamics of neural networks and ensuring efficient information flow. It guides the “Self-Proof-of-Work” process, favoring pathways that minimize “cost” or maximize “reward.”

7. A Computational Paradigm

The proposed explanation can be framed as a computational paradigm:

  1. Massive Parallel Input: High-bandwidth sensory input, filtered by the Principle of Photon Selection.

  2. Hierarchical Processing: Multi-layered, self-organizing neural networks extract features, build representations, and compress information.

  3. Continuous Self-Proof-of-Work: Information at each level undergoes continuous validation, ensuring consistency and minimizing prediction error.

  4. Constrained Serial Output: Low-bandwidth output, reflecting the highly processed and “proven” information that has passed the stringent Self-Proof-of-Work checks.

  5. Active Inference: The entire process can be modeled as an active inference system, constantly minimizing its free energy.

8. Implications and Future Research

This computational paradigm has several implications:

  • AGI Design: It suggests that AGI systems should not simply focus on maximizing input bandwidth or processing speed, but also on implementing mechanisms for self-organization, selective verification, and continuous self-proof-of-work.

  • Understanding Consciousness: The “Self-Proof-of-Work” process may be related to the emergence of consciousness, as it involves a continuous self-monitoring and self-validation of internal representations.

  • Neuroscience Research: It provides a new framework for interpreting neuroscientific data, suggesting that the brain’s architecture and dynamics may be optimized for efficient information processing and self-validation.

Future research directions include:

  • Developing computational models of the brain based on this paradigm.

  • Investigating the neural correlates of “Self-Proof-of-Work.”

  • Exploring the relationship between “Self-Proof-of-Work,” active inference, and the free energy principle.

  • Designing AGI systems that incorporate these principles.

9. Conclusion

The vast difference between the human brain’s input and output bandwidths can be understood through a computational paradigm that emphasizes self-organization, selective verification (photon selection), and continuous self-proof-of-work. The brain’s high input bandwidth is a consequence of parallel sensory processing, while its low output bandwidth reflects the computational cost of ensuring the validity, consistency, and effectiveness of expressed information. This perspective, inspired by GSISOM and an informational view of the universe, offers a novel framework for understanding brain function and designing intelligent systems. The “Self-Proof-of-Work” principle, in particular, highlights the active, ongoing process of validation that may be essential for both biological and artificial general intelligence.

Quote:Ground State Information Self-Organizing Model (GSISOM)
Quote:The Principle of Photon Selection
Quote:Self-Proof-of-Work: A Foundational Principle of Existence in the Informational Universe

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