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

Before-and-after with Control Designs

Image
Dr. Rishabh Pathak

Before-and-after with Control Designs are a type of informal experimental design where two areas or groups are selected, and the dependent variable is measured in both areas for an identical time period before the treatment is introduced. After the treatment is implemented in one area (the test area), the dependent variable is measured in both areas for an identical time period post-treatment. Here are the key characteristics of Before-and-after with Control Designs:


1.    Two Areas or Groups:

o    In this design, two areas or groups are involved: a test area/group where the treatment is applied and a control area/group where no treatment is applied. Data on the dependent variable are collected from both areas before and after the treatment.

2.    Pre- and Post-Treatment Measurements:

o    Researchers measure the dependent variable in both the test and control areas/groups for the same duration before the treatment is introduced. After the treatment is implemented in the test area/group, measurements are taken in both areas/groups for the same duration post-treatment.

3.    Comparison of Changes:

o    The treatment effect in Before-and-after with Control Designs is determined by comparing the change in the dependent variable in the test area/group with the change in the control area/group. This comparison helps assess the impact of the treatment while accounting for potential confounding factors.

4.    Control for Extraneous Variations:

o    By including a control group or area that does not receive the treatment, Before-and-after with Control Designs aim to control for extraneous variations that may influence the dependent variable. This design allows researchers to isolate the effects of the treatment from other factors.

5.    Avoidance of Extraneous Variation:

o    This design is considered superior to Before-and-after without Control Designs because it helps avoid extraneous variations resulting from the passage of time and non-comparability of the test and control areas. By comparing changes in both areas/groups, researchers can better attribute observed effects to the treatment.

6.    Enhanced Validity:

o    Before-and-after with Control Designs enhance the internal validity of the study by providing a basis for comparison between the effects of the treatment and the absence of treatment. This design allows for a more robust evaluation of the treatment's impact on the dependent variable.

7.    Practical Considerations:

o    Researchers may choose Before-and-after with Control Designs when historical data, time, or a comparable control area are available. This design offers a balance between simplicity and control over extraneous variables compared to other informal experimental designs.

Before-and-after with Control Designs offer a practical and comparative approach to studying the effects of interventions by including a control group or area for reference. By comparing changes in both the test and control groups, researchers can better assess the true impact of the treatment on the dependent variable while minimizing the influence of external factors.

 

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

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

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 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