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Unveiling Hidden Neural Codes: SIMPL – A Scalable and Fast Approach for Optimizing Latent Variables and Tuning Curves in Neural Population Data

Dr. Rishabh Pathak
This research paper presents SIMPL (Scalable Iterative Maximization of Population-coded Latents), a novel, computationally efficient algorithm designed to refine the estimation of latent variables and tuning curves from neural population activity. Latent variables in neural data represent essential low-dimensional quantities encoding behavioral or cognitive states, which neuroscientists seek to identify to understand brain computations better. Background and Motivation Traditional approaches commonly assume the observed behavioral variable as the latent neural code. However, this assumption can lead to inaccuracies because neural activity sometimes encodes internal cognitive states differing subtly from observable behavior (e.g., anticipation, mental simulation). Existing latent variable models face challenges such as high computational cost, poor scalability to large datasets, limited expressiveness of tuning models, or difficulties interpreting complex neural network-based functio...
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Benign Epileptiform Transients of Sleep Compared to Wicket Spikes

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Dr. Rishabh Pathak

Benign Epileptiform Transients of Sleep (BETS) and Wicket Spikes are two distinct EEG patterns that can be observed in recordings.

Distribution and Occurrence:

o Both BETS and Wicket Spikes have a similar distribution, often appearing in the temporal regions.

o BETS are commonly observed during light sleep, particularly in stages 1 and 2 of NREM sleep, while Wicket Spikes occur in wakefulness.

2.     Morphology and Waveform:

o BETS are sharply contoured, temporal region transients with a characteristically monophasic or diphasic waveform, showing an abrupt rise and steeper fall. They typically have an electronegative phase on the scalp.

o  Wicket Spikes, on the other hand, have a larger amplitude and a more variable waveform compared to BETS. They often resemble fragments of the wicket rhythm, which occurs within the background activity in the same distribution.

3.     Symmetry and Asymmetry:

o BETS typically exhibit shifting asymmetry with a symmetric lateralization, showing an equivalent number of transients on each side.

o  Wicket Spikes have a more symmetric waveform in the rise and fall, with the peak of the spike representing the sharply contoured side of an arciform wave.

4.    Clinical Context:

o  BETS are commonly considered a normal phenomenon and are often classified as benign, especially with accumulating evidence supporting their non-epileptic nature.

o  Wicket Spikes, on the other hand, may raise concerns due to their occurrence in wakefulness and their association with specific background activity patterns.

5.     Additional Features:

o  Other EEG patterns and activities, such as alpha rhythm changes, slow roving eye movements, and rhythmic mid-temporal theta activity, may accompany BETS, providing additional context for differentiation.

Understanding these differences in distribution, morphology, waveform, and clinical context is essential for accurately distinguishing between BETS and Wicket Spikes in EEG recordings, ensuring appropriate interpretation and clinical decision-making.

 

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