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

CAPS Utilizes A Lipid-Linked Mechanism For Priming Vesicle Exocytosis

Image
Dr. Rishabh Pathak

CAPS (Calcium-Dependent Activator Protein for Secretion) utilizes a lipid-linked mechanism for priming vesicle exocytosis, playing a crucial role in regulating neurotransmitter release at the synapse. Here is an overview of how CAPS functions in priming vesicle exocytosis through a lipid-linked mechanism:


1.      CAPS Protein Function:

o Regulatory Role: CAPS is a protein that acts as a calcium-dependent activator of vesicle priming and fusion at the presynaptic terminal.

o Priming Vesicle Exocytosis: CAPS facilitates the priming of synaptic vesicles, preparing them for fusion with the plasma membrane in response to neuronal activity.

2.     Lipid-Linked Mechanism:

o Phospholipid Binding: CAPS interacts with phospholipids, particularly phosphatidylinositol 4,5-bisphosphate (PIP2), which are essential components of the vesicle membrane.

o    Membrane Association: By binding to specific lipids on the vesicle membrane, CAPS localizes to the site of vesicle fusion, promoting the priming of vesicles for exocytosis.

3.     Calcium-Dependent Activation:

o    Calcium Sensing: CAPS contains calcium-binding domains that enable it to sense changes in intracellular calcium levels triggered by neuronal depolarization.

o    Activation of Priming: Upon calcium binding, CAPS undergoes conformational changes that enhance its ability to interact with phospholipids and SNARE proteins, promoting the priming of vesicles for exocytosis.

4.    Interaction with SNARE Proteins:

o    SNARE Complex Assembly: CAPS interacts with SNARE proteins, such as syntaxin and synaptobrevin, to facilitate the assembly of the SNARE complex, a key step in vesicle fusion.

o  Enhanced Fusion Readiness: By promoting SNARE complex formation, CAPS contributes to the readiness of vesicles for fusion with the plasma membrane during neurotransmitter release.

5.     Regulation of Neurotransmitter Release:

o    Enhanced Exocytosis: Through its lipid-linked mechanism and calcium-dependent activation, CAPS enhances the efficiency of vesicle priming and exocytosis, leading to increased neurotransmitter release at the synapse.

o    Fine-Tuning Synaptic Transmission: CAPS plays a critical role in fine-tuning synaptic transmission by regulating the availability of primed vesicles for fusion in response to neuronal signaling.

By utilizing a lipid-linked mechanism for priming vesicle exocytosis, CAPS contributes to the precise control of neurotransmitter release and synaptic communication. Understanding the molecular mechanisms by which CAPS regulates vesicle priming provides insights into the fundamental processes underlying synaptic function and offers potential targets for modulating synaptic transmission in health and disease.

 

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

Supervised Learning

Dr. Rishabh Pathak
What is Supervised Learning? ·     Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) are paired with the correct output (labels). The model learns to map inputs to outputs and can predict labels for unseen input data. ·     Goal: To learn a function that generalizes well from training data to accurately predict labels for new data. ·          Types: ·          Classification: Predicting categorical labels (e.g., classifying iris flowers into species). ·          Regression: Predicting continuous values (e.g., predicting house prices). Key Concepts: ·     Generalization: The ability of a model to perform well on previously unseen data, not just the training data. ·         Overfitting and Underfitting: ·    ...
Post a Comment
Read more