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

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...

Neuronal Precursor Proliferation Is Enhanced by Cannabinoids Via CB1/AKT/GSK- 3BETA/BETA-Catenin Signaling

The proliferation of neuronal precursors is enhanced by cannabinoids through a signaling pathway involving CB1 receptors, AKT, GSK-3beta, and beta-catenin. Here is a breakdown of the key points related to this mechanism:

1.      Cannabinoids and Neuronal Precursor Proliferation:

o Cannabinoids, including endocannabinoids and exogenous cannabinoids, have been shown to promote the proliferation of neuronal precursor cells in the brain.

o   This effect of cannabinoids on neuronal precursor proliferation is of interest for potential therapeutic applications in neuroregeneration and brain repair.

2.     CB1 Receptors:

o   Cannabinoid receptor type 1 (CB1) is a G protein-coupled receptor that is abundantly expressed in the brain, including regions involved in neurogenesis.

o  Activation of CB1 receptors by cannabinoids initiates intracellular signaling cascades that regulate various cellular processes, including neuronal precursor proliferation.

3.     AKT Signaling Pathway:

o  AKT, also known as protein kinase B, is a key signaling molecule involved in cell survival, proliferation, and growth.

o    Activation of CB1 receptors by cannabinoids can stimulate the AKT signaling pathway, leading to the activation of downstream effectors that promote neuronal precursor proliferation.

4.    GSK-3beta and Beta-Catenin:

o   Glycogen synthase kinase-3 beta (GSK-3beta) is a serine/threonine kinase that regulates various cellular functions, including cell proliferation and differentiation.

o  In the context of neuronal precursor proliferation, GSK-3beta is known to phosphorylate beta-catenin, a transcriptional co-activator involved in cell proliferation and survival.

o   Activation of AKT by CB1 receptor signaling can inhibit GSK-3beta activity, leading to the stabilization and accumulation of beta-catenin in the nucleus.

5.     CB1/AKT/GSK-3beta/Beta-Catenin Signaling:

o The CB1/AKT/GSK-3beta/beta-catenin signaling pathway represents a mechanism through which cannabinoids enhance the proliferation of neuronal precursor cells.

o  Activation of CB1 receptors by cannabinoids triggers a cascade of events that ultimately result in the activation of AKT, inhibition of GSK-3beta, and nuclear translocation of beta-catenin, promoting cell proliferation.

6.    Therapeutic Implications:

o  Understanding the molecular mechanisms underlying the effects of cannabinoids on neuronal precursor proliferation can inform the development of novel therapeutic strategies for promoting neurogenesis and brain repair in various neurological conditions.

o  Targeting the CB1/AKT/GSK-3beta/beta-catenin pathway may offer potential therapeutic opportunities for enhancing neuroregeneration and functional recovery in the brain.

In summary, cannabinoids enhance neuronal precursor proliferation through the CB1/AKT/GSK-3beta/beta-catenin signaling pathway, highlighting the potential of cannabinoid-based therapies for promoting neurogenesis and brain repair.

 

Comments

Popular posts from this blog

Relation of Model Complexity to Dataset Size

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....

Linear Models

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...

Predicting Probabilities

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 ...

Uncertainty in Multiclass Classification

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 ...

Conducting a Qualitative Analysis

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...