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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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Types of Ictal Epileptiform Patterns

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Dr. Rishabh Pathak

Several types of ictal epileptiform patterns, particularly focusing on focal-onset and generalized-onset seizures.

1.      Focal-Onset Seizures:

o  Characteristics: The ictal pattern for focal-onset seizures is defined by the EEG findings present during the seizure. These patterns are often stereotyped for the individual patient and typically include evolving rhythms or repetitive sharp waves.

o Evolution: The ictal activity usually demonstrates clear evolution, which can include changes in frequency, amplitude, distribution, and waveform. This evolution is crucial for identifying the seizure onset.

o  Duration: Focal-onset seizure patterns generally last several seconds, distinguishing them from other patterns like the fourteen and six positive spikes (14&6), which last less than 2 seconds.

2.     Generalized-Onset Seizures:

o  Characteristics: Ictal patterns for generalized-onset seizures differ from those of focal-onset seizures. They tend to show greater similarity between their ictal and interictal EEG patterns.

o  Variability: The ictal patterns for generalized seizures can vary based on the type of seizure, which is not the case for focal-onset seizures.

3.     Non-Evolving Patterns:

o Description: Infrequently, the ictal pattern may not include evolution and can manifest as desynchronization, regular repetitive spikes, or regular rhythmic slowing. These patterns are more commonly associated with focal motor seizures without cognitive impairment.

4.    Secondary Bilateral Synchrony:

o    Occurrence: While focal-onset seizures typically do not present with bilateral fields at their onset, secondary bilateral synchrony can occur. This is an exception and does not represent the typical pattern for focal-onset seizures.

5.     Behavioral Correlation:

o Stereotyped Behavioral Change: Ictal patterns are usually accompanied by a stereotyped behavioral change, which is a critical feature for identifying seizures. However, some focal seizures may occur without noticeable behavioral changes, making it essential to consider cognitive testing to determine if a seizure has occurred.

These types of ictal patterns are essential for clinicians to recognize and differentiate during EEG interpretation, as they provide vital information for diagnosing and managing epilepsy.

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