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Energy Manifold Natural Gradient Descent: From Riemannian Optimization to Modern Neuroscience, NeuroAI and Quantum Physics

When Geometry, Energy, Artificial Intelligence and Neuroscience Converge Modern Artificial Intelligence is rapidly moving beyond the idea that learning simply means minimizing an error function. Increasingly, researchers are asking a deeper question: what is the structure of the space in which learning takes place? This question becomes particularly important when the system being modelled is constrained, nonlinear, dynamic, or governed by physical principles. A recent work titled “Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers” , by Zhangyong Liang and Huanhuan Gao, introduces Energy Manifold Natural Gradient Descent (EMNGD) , a mathematical framework that extends energy-based natural-gradient optimization from unconstrained Euclidean parameter spaces to constrained Riemannian parameter manifolds . At its core, the framework proposes a simple but powerful principle: An optimization algorithm should not only determine how to reduce error; it sh...

Changes in the Brain can be shown at many levels of analysis

Changes in the brain can be observed and studied at various levels of analysis, providing insights into the mechanisms underlying brain plasticity and behavior. Here are different levels of analysis where changes in the brain can be demonstrated:


1.     Behavioral Changes: Behavioral changes are often the most visible indicators of brain plasticity. Alterations in behavior, such as learning new skills, adapting to new environments, or responding to stimuli, reflect underlying changes in neural circuits and synaptic connections.


2.  Global Measures of Brain Activity: Techniques such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG) allow researchers to observe changes in brain activity at a macroscopic level. These imaging methods provide insights into overall brain function and connectivity.


3.  Synaptic Changes: Synaptic plasticity plays a crucial role in learning and memory processes. Changes in synaptic strength, formation of new synapses, and pruning of existing synapses can be studied at the level of individual synapses to understand how neural networks adapt to experiences.


4.    Molecular Processes: Molecular changes within neurons, such as modifications in ion channels, gene expression, and protein synthesis, underlie synaptic plasticity and long-term changes in brain function. Studying molecular processes provides a detailed understanding of the cellular mechanisms driving brain plasticity.


5.  Anatomical Changes: Structural changes in the brain, including alterations in neuronal morphology, dendritic arborization, and axonal growth, can be visualized using techniques like electron microscopy and immunohistochemistry. Anatomical changes reflect the structural reorganization of neural circuits in response to experiences.


6.  Physiological Changes: Physiological measures, such as changes in neuronal excitability, neurotransmitter release, and synaptic transmission, offer insights into the functional adaptations of the brain. Studying physiological changes helps link cellular processes to behavioral outcomes.


By examining changes in the brain at multiple levels of analysis, researchers can gain a comprehensive understanding of how neural plasticity shapes behavior and cognition. Integrating findings from different levels of analysis provides a holistic view of brain function and adaptation to environmental stimuli.

 

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