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
Image
Dr. Rishabh Pathak

The Food and Drug Administration (FDA) is a regulatory agency within the United States Department of Health and Human Services responsible for protecting and promoting public health by ensuring the safety and efficacy of food, drugs, medical devices, cosmetics, and other products. Here is an overview of the FDA and its key functions:


1.      Regulatory Oversight:

o    The FDA regulates a wide range of products, including:

§  Food: Ensuring the safety and labeling of food products, dietary supplements, and additives.

§Drugs: Evaluating the safety, efficacy, and quality of pharmaceuticals for human use.

§  Medical Devices: Reviewing and approving medical devices to ensure their safety and effectiveness.

§  Biologics: Regulating biological products such as vaccines, blood products, and gene therapies.

§  Cosmetics: Monitoring the safety and labeling of cosmetic products.

§ Tobacco Products: Regulating the manufacturing, distribution, and marketing of tobacco products.

2.     Functions and Responsibilities:

o  Approval Process: The FDA reviews and approves new drugs, medical devices, and biologics through rigorous evaluation of clinical data to ensure their safety and efficacy before they can be marketed and sold to the public.

o Post-Market Surveillance: The FDA monitors products after approval to identify and address safety concerns, adverse events, and product recalls.

o  Labeling and Advertising: The FDA regulates the labeling and advertising of products to ensure accurate information is provided to consumers and healthcare professionals.

o  Inspections and Compliance: The FDA conducts inspections of manufacturing facilities to ensure compliance with regulatory standards and good manufacturing practices.

o  Public Health Education: The FDA provides information to the public about health risks, recalls, and regulatory actions to promote public health and safety.

3.     Drug Development Process:

o  The FDA plays a crucial role in the drug development process by reviewing investigational new drug applications (INDs), new drug applications (NDAs), and abbreviated new drug applications (ANDAs) submitted by pharmaceutical companies.

o  The FDA evaluates preclinical and clinical data to assess the safety and efficacy of new drugs, determining whether they can be approved for marketing and distribution.

4.    International Collaboration:

o    The FDA collaborates with regulatory agencies worldwide to promote global health and harmonize regulatory standards for products that are traded internationally.

oThrough initiatives like the International Conference on Harmonisation (ICH), the FDA works with international partners to streamline regulatory processes and facilitate the development of safe and effective products.

5.     Consumer Protection:

o    The FDA's primary mission is to protect and promote public health by ensuring the safety, efficacy, and security of products under its regulatory purview.

o  By setting and enforcing standards for product quality, manufacturing practices, and labeling, the FDA aims to safeguard consumers from potential health risks and fraudulent products.

In summary, the Food and Drug Administration (FDA) is a regulatory agency that plays a critical role in safeguarding public health by regulating the safety, efficacy, and quality of food, drugs, medical devices, and other products. Through its oversight, approval processes, post-market surveillance, and public health education efforts, the FDA works to ensure that products available to consumers meet established standards and contribute to the well-being of the population.



 

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

Ensembles of Decision Trees

Dr. Rishabh Pathak
1. What are Ensembles? Ensemble methods combine multiple machine learning models to create more powerful and robust models. By aggregating the predictions of many models, ensembles typically achieve better generalization performance than any single model. In the context of decision trees, ensembles combine multiple trees to overcome limitations of single trees such as overfitting and instability. 2. Why Ensemble Decision Trees? Single decision trees: Are easy to interpret but tend to overfit training data, leading to poor generalization,. Can be unstable because small variations in data can change the structure of the tree significantly. Ensemble methods exploit the idea that many weak learners (trees that individually overfit or only capture partial patterns) can be combined to form a strong learner by reducing variance and sometimes bias. 3. Two Main Types of Tree Ensembles (a) Random Forests Random forests are ensembles con...
Post a Comment
Read more

Uncertainty Estimates from Classifiers

Dr. Rishabh Pathak
1. Overview of Uncertainty Estimates Many classifiers do more than just output a predicted class label; they also provide a measure of confidence or uncertainty in their predictions. These uncertainty estimates help understand how sure the model is about its decision , which is crucial in real-world applications where different types of errors have different consequences (e.g., medical diagnosis). 2. Why Uncertainty Matters Predictions are often thresholded to produce class labels, but this process discards the underlying probability or decision value. Knowing how confident a classifier is can: Improve decision-making by allowing deferral in uncertain cases. Aid in calibrating models. Help in evaluating the risk associated with predictions. Example: In medical testing, a false negative (missing a disease) can be worse than a false positive (extra test). 3. Methods to Obtain Uncertainty from Classifiers 3.1 ...
Post a Comment
Read more