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

Dr. Rishabh Pathak
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...
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Greebles

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Dr. Rishabh Pathak

Greebles are a category of computer-generated novel objects that were originally designed as a control set for studying face recognition and perceptual expertise. 

1.     Definition:

    • Greebles are artificial, three-dimensional objects created for research purposes to investigate visual perception, object recognition, and cognitive processes related to expertise and categorization.
    • Unlike natural objects or faces, Greebles have no real-world counterparts and are specifically designed to be visually complex stimuli for experimental studies.

2.     Purpose:

    • Greebles were developed by psychologists as a non-face control stimulus to study face recognition abilities and perceptual expertise in individuals, particularly in the context of cognitive neuroscience and experimental psychology.
    • By using Greebles as stimuli in research experiments, scientists can explore how individuals perceive and categorize complex visual stimuli that do not have inherent meaning or familiarity.

3.     Research Applications:

    • Greebles have been widely used in cognitive psychology and neuroscience research to investigate visual processing, object recognition, perceptual learning, and the development of expertise in visual tasks.
    • Studies using Greebles have provided insights into the neural mechanisms underlying face perception, the effects of training on object recognition, and the generalization of perceptual skills to novel stimuli.

4.     Experimental Design:

    • Researchers often use Greebles in controlled experiments to assess participants' ability to discriminate between different Greeble exemplars, detect subtle variations in Greeble configurations, and generalize learning to new Greeble stimuli.
    • By manipulating Greeble features, orientations, and configurations, researchers can study how perceptual expertise develops, how visual discrimination skills improve with practice, and how the brain processes complex visual information.

5.     Contribution to Science:

    • The use of Greebles in experimental studies has advanced our understanding of visual perception, object recognition, and the neural mechanisms involved in processing novel and complex visual stimuli.
    • Greebles have provided researchers with a valuable tool for studying cognitive processes, perceptual learning, and the development of expertise in visual tasks, offering insights into the organization and plasticity of the human brain.

In summary, Greebles are artificial objects created for research purposes to study visual perception, object recognition, and cognitive processes related to expertise and categorization. Their use in experimental studies has contributed to our understanding of visual processing, perceptual learning, and the neural mechanisms underlying complex visual tasks in cognitive psychology and neuroscience research.

 

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