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...
Benign
Epileptiform Transients of Sleep (BETS) have distinguishing features that
differentiate them from other EEG patterns.
Waveform
Characteristics:
o BETS are sharply
contoured, temporal region transients that commonly occur during light sleep,
particularly in stages 1 and 2 of NREM sleep.
o The waveform of
BETS is characteristically monophasic or diphasic, with one principal phase
showing an abrupt rise and an even steeper fall. This phase is typically
electronegative on the scalp.
o Some BETS may
exhibit an after-going slow wave, although this feature is less common.
2. Amplitude and
Duration:
o BETS are
typically less than 90 ÎĽV in amplitude and 90 milliseconds in duration, with
the average amplitude and duration being around 60 ÎĽV and 60 milliseconds,
respectively.
o The term "small
sharp spikes" is sometimes used to describe BETS due to their typical
size, but larger and longer BETS can also occur, highlighting variability in
their characteristics.
3. Occurrence and
Distribution:
o BETS often occur
in multiple occurrences within a recording, with several similarly formed
temporal spikes observed during drowsiness or light sleep.
o The shifting
lateralization of BETS should be symmetric, with an equivalent number of BETS
on each side. Recurrence on one side is typically separated by more than 1
second and often more than 10 seconds.
4. Localization and
Field Distribution:
o BETS are almost
always centered in the mid-temporal region, extending over the entire temporal
lobe and sometimes involving the adjacent frontal lobe.
o The best montages
for observing BETS are those utilizing a contralateral reference electrode,
which can show a transverse dipole with a negative phase reversal over one
temporal lobe and a positive one over the other.
Understanding
these distinguishing features of BETS is essential for accurate EEG
interpretation and differentiation from other transient EEG patterns or
epileptiform discharges.
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