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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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Clinical significance of Benign Epileptiform Transients of Sleep

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Dr. Rishabh Pathak

Benign Epileptiform Transients of Sleep (BETS) are commonly considered a normal phenomenon in EEG recordings, especially in the context of sleep. 


1.      Normal Variant:

o BETS are often classified as benign and are considered a normal EEG pattern, particularly during light sleep stages such as stages 1 and 2 of non-rapid eye movement (NREM) sleep.

o Accumulating evidence supports the non-epileptic nature of BETS, indicating that they are typically not indicative of underlying pathological conditions.

2.     Age-Related Occurrence:

o BETS are more commonly observed in adults between 30 and 60 years of age, with children younger than 10 years rarely exhibiting them.

o The age-related distribution of BETS can provide additional context for their interpretation in EEG recordings.

3.     Distinguishing from Pathological Patterns:

o While BETS are generally considered benign, their distinction from epileptiform discharges and other pathological patterns is crucial for accurate EEG interpretation.

o Understanding the characteristic features of BETS, such as their waveform, distribution, and occurrence during sleep, helps differentiate them from abnormal epileptiform activity.

4.    Diagnostic Considerations:

o The presence of BETS in EEG recordings may prompt further evaluation to confirm their benign nature and rule out any underlying epileptic activity, especially in cases where differentiation from pathological patterns is challenging.

o Knowledge of the typical characteristics and clinical significance of BETS aids clinicians in making informed decisions regarding patient management and treatment.

5.     Research and Clinical Practice:

o Studies investigating the source and characteristics of BETS contribute to the understanding of normal EEG variants and help refine EEG interpretation practices.

o The clinical significance of BETS lies in their recognition as a common and typically benign EEG pattern, highlighting the importance of accurate interpretation and differentiation from pathological findings.

Overall, recognizing the clinical significance of BETS as a normal variant in EEG recordings is essential for accurate interpretation, appropriate patient management, and the avoidance of unnecessary interventions based on benign transient patterns.

 

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