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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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Electroretinogram Artifact

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Dr. Rishabh Pathak

Electroretinogram (ERG) artifacts in EEG recordings are related to retinal activity and can produce specific patterns.

1.      Nature of Electroretinogram (ERG) Artifacts:

o ERG artifacts are produced by retinal activity in response to photic stimulation.

o These artifacts are distinct from photomyogenic artifacts and have specific waveform characteristics.

2.     Characteristics:

o ERG artifacts manifest as low-amplitude frontal artifacts from the retina that are time-locked to the strobe.

o  The waveform of ERG artifacts is monomorphic and less spike-like compared to other artifacts, with a field typically limited to specific frontal electrodes.

3.     Differentiation:

o  Covering an eye during stimulation can help diminish the artifact from the ipsilateral frontal polar electrode, aiding in identifying and distinguishing ERG artifacts from other types of artifacts.

o  Understanding the waveform and field distribution of ERG artifacts is essential for accurate interpretation and differentiation from pathological brain activity or other types of artifacts in EEG recordings.

Recognizing the unique characteristics of ERG artifacts and their association with retinal activity can assist in accurate EEG interpretation and the differentiation of these artifacts from other EEG patterns or abnormalities.

 

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