Tutorial on the Logic Model of the Human Brain: Quantum Information, DNA, and the Emergence of Intelligence

Abstract

In this paper, we present a new approach to understanding how the human brain works by proposing the Logic Model, a framework in which the brain processes information through quantum mechanisms. Building on the conventional neurological paradigm, we suggest that quantum processes within DNA—particularly through quantum tunneling of hydrogen bonds—introduce quantum randomness that is processed probabilistically into symbolic algorithms. We connect this framework to logical independence, as discussed in the work of Paterek et al., proposing that human cognition emerges through finite probabilities and the non-local interactions between neurons. This model offers a novel understanding of intelligence as arising from quantum information processing within the brain.

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### 1. Introduction: The Conventional Paradigm

To understand the Logic Model, we first examine the classical, widely accepted theory of brain function—the conventional paradigm—which primarily relies on classical neuroscience.

#### 1.1 How Neurons Work in the Classical Model

The brain consists of approximately 86 billion neurons, which communicate via electrical impulses called action potentials. These impulses are like messages sent across wires, where each signal is a piece of information that informs the brain's activities—whether it’s moving a muscle, recalling a memory, or processing sensory inputs.

- Action potentials are triggered by ions moving across the neuron’s membrane, changing the electrical charge in a controlled way.

- Neurons connect via synapses, where neurotransmitters carry signals between cells.

In this model, the brain behaves like an incredibly intricate electrical circuit, where signals are transmitted through structured pathways that lead to cognitive processes.

#### 1.2 Energy Efficiency and Information Processing

Despite its complexity, the brain is remarkably energy-efficient. Each spike from a neuron uses around 1 nanojoule of energy. Given the scale of neuron activity, the brain remains energy-efficient compared to typical computers.

Using Landauer’s principle, we estimate the minimum energy required to process information. Landauer’s principle states that erasing one bit of information requires 2.97 × 10?21 joules of energy. Based on this, each neuron has the potential to process billions of bits per second. However, this classical framework doesn’t fully explain the brain’s abilities, especially its creativity, problem-solving, and adaptability. To address this, we introduce a quantum information processing model.

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### 2. The Logic Model: Quantum Information Processing in the Brain

The Logic Model goes beyond the classical idea of electrical signaling. We propose that the brain also processes information via quantum mechanics—the strange and highly non-deterministic laws that govern particles like electrons. We suggest that DNA in neurons plays a key role in quantum information processing.

#### 2.1 Quantum Tunneling and DNA

DNA carries the genetic code for life and has a double-helix structure, like a twisted ladder. The “rungs” of this ladder are made up of base pairs (A-T and G-C), which are held together by hydrogen bonds. At this molecular level, quantum mechanics becomes important.

In quantum mechanics, particles can do things that seem impossible in classical physics, such as quantum tunneling, where particles pass through energy barriers they shouldn’t be able to cross. In DNA, hydrogen atoms in the bonds between base pairs can quantum tunnel at terahertz frequencies (trillions of cycles per second), creating tiny, fast quantum events.

- Quantum tunneling introduces quantum randomness—truly random events that don’t have hidden causes, unlike classical randomness, which is simply hard to predict.

#### 2.2 The Vector Potential and Neurons

When neurons fire, ions (charged atoms) move across the membrane, generating electromagnetic fields. If ions move in parallel, their magnetic fields may cancel out, but a vector potential (\(\mathbf{A}\)) can remain. This field influences charged particles and extends across neighboring neurons, linking them in non-local ways that classical models don’t explain.

- The vector potential could allow neurons to interact via quantum connections, creating a non-local quantum field. This suggests that neurons far apart can influence each other through quantum effects, rather than just by local synaptic signals.

#### 2.3 Quantum Randomness and DNA Processing

The quantum tunneling in DNA introduces quantum randomness into the brain. In the Logic Model, this randomness isn’t noise; instead, it’s processed into meaningful information. The DNA acts as a quantum processor, turning these random quantum events into patterns that the brain can use.

- Each quantum random event has a finite probability of being turned into a symbolic algorithm—a set of instructions that the brain uses for cognition, such as thinking, memory, and learning. This ties into algorithmic information theory, which we explore in the next section.

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### 3. Kolmogorov Complexity and Quantum Randomness

Kolmogorov complexity measures the complexity of a system by how simple an algorithm can describe it. For example, a repeating pattern has low complexity, while a random string of numbers has high complexity because it requires a long description.

#### 3.1 Finite Probabilities and Symbolic Algorithms

In the Logic Model, the brain processes quantum random events into finite symbolic algorithms—instructions the brain uses to perform cognitive functions like problem-solving and decision-making. Quantum randomness provides the raw data, and DNA processes these inputs into structured information, similar to how computers process data into outputs.

#### 3.2 The Role of Logical Independence

In their paper, Paterek et al. discuss logical independence in quantum systems, meaning that quantum events are not determined by previous states. This independence aligns with our Logic Model, where quantum random events in the brain’s DNA are logically independent, allowing for unpredictable and novel information processing. This unpredictability could explain human creativity and adaptability.

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### 4. Non-Local Quantum Coherence and Intelligence

In quantum mechanics, non-locality means that particles can be entangled, influencing each other instantly, even when far apart. In the Logic Model, neurons can become entangled through the vector potential, allowing for non-local quantum coherence across the brain.

#### 4.1 Quantum Coherence in Neurons

In our model, quantum coherence allows neurons to process information simultaneously across different regions, integrating information to form a unified, coherent cognitive experience. This kind of coherence would be far more efficient than classical information processing models.

#### 4.2 Emergence of Intelligence Through Quantum Processing

We propose that intelligence emerges from the brain’s processing of quantum random events into symbolic algorithms. The brain continuously generates new algorithms from quantum randomness, allowing for complex behaviors, creativity, and high-level cognitive functions.

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### 5. Integrating Logical Independence with the Logic Model

#### 5.1 Logical Independence in DNA Quantum Processes

Paterek et al.’s idea of logical independence fits well with the Logic Model. Each quantum random event in DNA is independent of the past, meaning the brain is constantly generating new information. This provides a theoretical basis for how humans can come up with novel ideas and creative solutions.

#### 5.2 Non-Local Quantum Coherence and Logical Independence

The non-local quantum fields in the brain allow neurons to share quantum information across distances. Even though quantum events are logically independent, the brain can process them coherently, leading to the integration of information across different regions. This could explain the brain's ability to handle complex tasks and generate intelligence.

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### 6. Conclusion: The Logic Model and the Emergence of Intelligence

The Logic Model offers a new way of understanding human intelligence by combining quantum mechanics, biology, and information theory. By incorporating quantum randomness, logical independence, and non-local quantum coherence, we suggest that human cognition is the result of probabilistic quantum processing in DNA. The brain is not only a classical machine but a quantum information processor that continuously generates symbolic algorithms from quantum events, leading to creativity, problem-solving, and adaptability.

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### References

1. McFadden, J., & Al-Khalili, J. (2014). Life on the Edge: The Coming of Age of Quantum Biology. Crown Publishers.

2. Penrose, R. (1994). Shadows of the Mind: A Search for the Missing Science of Consciousness. Oxford University Press.

3. Chaitin, G. J. (1966). On the Length of Programs for Computing Finite Binary Sequences. Journal of the ACM, 13(4), 547-569.

4. Tegmark, M. (2000). Importance of Quantum Decoherence in Brain Processes. Physical Review E, 61(4), 4194–4206.

5. Paterek, T., Kofler, J., Prevedel, R., Klimek, P., Aspelmeyer, M., Zeilinger, A., & Brukner, ?. (2009). Logical Independence and Quantum Randomness. New Journal of Physics, 11(11), 113019.

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This tutorial, authored by Sammy K. Brown, CEO of Averoses Inc., introduces the Logic Model as a quantum-based theory of brain function and intelligence. This version is designed to explain the concepts in a more