[otikik]

> Physics can't deal with the brain. No equation can be written.

There are many many many physics simulations out there that cannot be "written with an equation". Climate Modelling, for example. You cannot write a single equation to model all that. You need a big complex piece of software, made of many equations, a lot of hardware, and a lot of processing time. Any of those was simply inconceivable mere decades ago.

It's possible that it's as you say, and the brain is inscrutable if we attack the problem from the physics point of view alone.

I think that you may be right. With what we have now. But decades from now? I'm not so sure.

[ajani]

All climate models are based on mathematical physics models. I don't know the specifics, so I asked chatGPT and here is what it said:

'''

Climate modeling is a multifaceted field rooted in physics that relies on a complex set of equations to describe various atmospheric, oceanic, and terrestrial processes. Here's an overview of the key equations that form the foundation of climate models:

Navier-Stokes Equations: Governing the flow of fluids like the atmosphere and oceans, these equations capture how the velocity of a fluid changes over time.

Radiative Transfer Equations: Essential for understanding how sunlight and other forms of radiation interact with the atmosphere, including scattering, absorption, and emission.

Energy Balance Models: These equations describe the balance between incoming solar energy and outgoing heat, fundamental for capturing the planet's energy dynamics.

Equations of State: Linking density, pressure, and temperature, these equations are critical for understanding the behavior of the atmosphere and ocean.

Continuity Equations: Representing the conservation of mass in the atmosphere and oceans.

Moist Processes Equations: Capturing phase changes between water vapor, liquid water, and ice, along with latent heat exchange.

Boundary Layer Equations: Describing the complex dynamics near Earth's surface where the atmosphere interacts with the land or ocean.

Chemical and Aerosol Equations: Governing the reactions and interactions between different chemical species and particles, which can affect both weather and climate.

Sea Ice and Glacial Equations: Modeling the flow and melting of ice, essential for understanding the cryosphere.

These equations are solved numerically using computer algorithms, often over a grid representing the Earth's surface and atmosphere. Together, they form an interconnected system that allows scientists to simulate and analyze the climate system's behavior. This intricate mathematical framework underscores how the study of climate is fundamentally rooted in mathematical and physical principles.

'''

There are no such equations for the brain.

[otikik]

Well you should have asked ChatGPT directly then:

In the context of human behavior, consciousness and neurology, what are the mathematical equations that are relevant in order to model how a human brain works? Please highlight any equation that involves Physics in particular

ChatGPT:

Modeling the human brain is an extremely complex task, and it involves various levels of abstraction and different mathematical and computational approaches. While there are no specific equations that fully capture the intricacies of the brain's function, several mathematical and physics-related principles can be applied at different levels of analysis. Here are some relevant equations and concepts:

Neuron Modeling:

Hodgkin-Huxley Equations: These equations describe the behavior of ion channels in neuron membranes and are fundamental to understanding how neurons generate action potentials. Integrate-and-Fire Models: Simpler models like the Leaky Integrate-and-Fire model can describe the basic firing behavior of neurons using differential equations. Neural Networks:

Backpropagation:

This is a fundamental algorithm for training artificial neural networks. It involves the chain rule from calculus to update weights during training. Activation Functions: Functions like the sigmoid, ReLU (Rectified Linear Unit), and softmax are used to model the activation of neurons in artificial neural networks. Synaptic Plasticity:

Hebbian Learning:

Often described as "cells that fire together wire together," it's a principle used to model how synaptic connections strengthen or weaken based on correlated firing patterns.

Diffusion Equations:

Fick's Law of Diffusion: Relevant for modeling the diffusion of neurotransmitters across synapses. Information Theory:

Shannon's Information Theory:

While not an equation per se, concepts like entropy and mutual information are used to quantify information flow and coding in neural systems.

Quantum Mechanics:

While not directly related to classical neuroscience, there's ongoing research into whether quantum effects play a role in brain function. This would involve quantum mechanics equations such as the Schrödinger equation and the equations describing quantum states. Connectome Modeling:

Graph Theory:

Modeling the brain as a network involves concepts from graph theory, like adjacency matrices and graph algorithms. While not a direct physics equation, it's relevant for understanding brain connectivity. Functional Imaging:

Techniques like Functional Magnetic Resonance Imaging (fMRI) rely on physics equations related to magnetism and nuclear magnetic resonance to measure brain activity indirectly. Biomechanics:

Equations from mechanics can be used to model the physical properties of the brain, like deformation during injury or the propagation of mechanical waves in brain tissue.

Electroencephalography (EEG):

Maxwell's Equations: EEG measures electrical potentials on the scalp, and the interpretation of these signals relies on Maxwell's equations describing the behavior of electric fields. It's important to note that modeling the human brain is still an active area of research, and there's no single mathematical framework that fully explains all aspects of brain function. Instead, a multi-disciplinary approach is used, combining mathematics, physics, biology, and computer science to gain a better understanding of the brain's complexity at various scales, from individual neurons to large-scale brain networks.

[ajani]

> While there are no specific equations that fully capture the intricacies of the brain's function..

That.

[otikik]

No equation “captures” reality, in any branch of science. They all “model” reality. Which means that some details (intricacies) are always ignored.

[ajani]

Yes but resolution and coherence matters. A map that says "here be dragons" is not high resolution. A map that misidentifies or misses parts is incoherent. The brain is not physical (as explained above), and cannot be captured in a sufficiently high resolution map that only studies substance and its interactions. Since we have no access to the formwork, we will always have missing data, that hampers our ability to create such a coherent map.

Thank you for staying engaged, it's helping me see my position more clearly.