Skip to main content

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...
Post a Comment

Ictal Epileptiform Patterns Compared to Fourteen and Six Per Second Positive Spikes

Image
Dr. Rishabh Pathak


When comparing ictal epileptiform patterns to fourteen and six per second positive spikes (14&6), several distinguishing features can be identified.

1.      Duration:

o  Ictal Patterns: Ictal patterns for focal-onset seizures typically last several seconds or longer. They are characterized by sustained activity that evolves over time.

o  14&6 Spikes: The 14&6 positive spikes usually last less than 1 second and rarely extend beyond 2 seconds. This brief duration is a significant distinguishing feature.

2.     Distribution:

o    Ictal Patterns: Ictal patterns often begin in a focal area and may evolve to involve broader regions of the cortex. They are less likely to present bilaterally at onset.

o 14&6 Spikes: The 14&6 pattern can occur bilaterally, either synchronously or asynchronously. This bilateral occurrence is a key differentiator, as focal-onset seizures typically do not have bilateral fields at their onset.

3.     Evolution:

o  Ictal Patterns: Ictal patterns are characterized by clear evolution, which may include changes in frequency, amplitude, and waveform. This evolution is crucial for identifying the onset of a seizure.

o 14 & 6 Spikes: The 14&6 pattern may show some evolving characteristics but is generally more stable and does not demonstrate the same level of progressive change as ictal patterns.

4.    Clinical Significance:

o  Ictal Patterns: The presence of ictal patterns is clinically significant as they indicate the occurrence of a seizure. They are associated with behavioral changes and can lead to cognitive impairment.

o  14&6 Spikes: While the 14&6 pattern may appear suggestive of an ictal pattern, it is not necessarily indicative of a seizure. It can occur in various contexts and does not have the same clinical implications as ictal patterns.

5.     Association with Behavioral Changes:

o Ictal Patterns: Ictal patterns are typically associated with stereotyped behavioral changes, which are critical for seizure identification.

o  14&6 Spikes: The 14&6 pattern does not have a consistent association with behavioral changes indicative of seizure activity.

6.    Electrographic Features:

o    Ictal Patterns: Ictal patterns may include a variety of electrographic features, such as rhythmic slowing, spikes, and sharp waves, which evolve over the course of the seizure.

o 14&6 Spikes: The 14&6 pattern is characterized by its specific frequency and morphology, which can be mistaken for ictal activity but lacks the complexity and evolution of true ictal patterns.

In summary, while both ictal epileptiform patterns and fourteen and six per second positive spikes may present as rhythmic activity on EEG, they differ significantly in terms of duration, distribution, evolution, clinical significance, and association with behavioral changes. Understanding these distinctions is essential for accurate EEG interpretation and seizure diagnosis.

Categories:

Comments

Post a Comment

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Dr. Rishabh Pathak
Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....
Post a Comment
Read more

Linear Models

Dr. Rishabh Pathak
1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...
Post a Comment
Read more

Predicting Probabilities

Dr. Rishabh Pathak
1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...
Post a Comment
Read more

Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
Post a Comment
Read more

Conducting a Qualitative Analysis

Dr. Rishabh Pathak
Conducting a qualitative analysis in biomechanics involves a systematic process of collecting, analyzing, and interpreting non-numerical data to gain insights into human movement patterns, behaviors, and interactions. Here are the key steps involved in conducting a qualitative analysis in biomechanics: 1.     Data Collection : o     Use appropriate data collection methods such as video recordings, observational notes, interviews, or focus groups to capture qualitative information about human movement. o     Ensure that data collection is conducted in a systematic and consistent manner to gather rich and detailed insights. 2.     Data Organization : o     Organize the collected qualitative data systematically, such as transcribing interviews, categorizing observational notes, or indexing video recordings for easy reference during analysis. o     Use qualitative data management tools or software to f...
Post a Comment
Read more