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

Correlational Research Design

Image
Dr. Rishabh Pathak

Correlational research design is a type of non-experimental research design that focuses on examining the relationships between variables without manipulating them. In correlational studies, researchers investigate the extent to which changes in one variable are associated with changes in another variable. Here are key characteristics and components of correlational research design:


1. Relationship Between Variables: Correlational research aims to identify and describe the relationships between two or more variables. Researchers seek to determine whether changes in one variable are related to changes in another variable, without implying causation.


2. No Manipulation of Variables: Unlike experimental research, correlational research does not involve manipulating independent variables to observe their effects on dependent variables. Instead, researchers measure variables as they naturally occur and examine how they are related to each other.


3.Quantitative Analysis: Correlational research typically involves quantitative data analysis to assess the strength and direction of relationships between variables. Statistical techniques such as correlation coefficients, regression analysis, and scatterplots are used to analyze and interpret the data.


4.    Correlation Coefficients: Correlation coefficients, such as Pearson's r or Spearman's rho, are commonly used in correlational research to quantify the degree and direction of the relationship between variables. The correlation coefficient ranges from -1 to +1, with values closer to -1 or +1 indicating stronger relationships.


5. Direction and Strength of Relationships: Correlational research examines both the direction (positive or negative) and the strength (weak, moderate, strong) of relationships between variables. Positive correlations indicate that variables move in the same direction, while negative correlations suggest they move in opposite directions.


6. Cross-Sectional Design: Correlational research often uses a cross-sectional design, where data is collected at a single point in time to assess relationships between variables. Longitudinal studies, which track variables over time, can also be used to examine changes in relationships.


7. Third Variable Problem: Correlational research is susceptible to the third variable problem, where an unmeasured variable may influence the relationship between the variables of interest. Researchers must consider potential confounding variables that could impact the observed correlations.


8.    Predictive Value: Correlational research can have predictive value by identifying patterns of association between variables. Researchers can use correlational findings to make predictions about one variable based on the values of another variable, although causation cannot be inferred.


9. Applications: Correlational research is widely used in psychology, sociology, education, and other social sciences to explore relationships between variables such as academic performance and study habits, stress levels and health outcomes, or job satisfaction and productivity.


Correlational research design provides valuable insights into the relationships between variables and helps researchers understand patterns of association in natural settings. By examining correlations between variables, researchers can identify potential connections, make predictions, and generate hypotheses for further investigation.

 

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

Different Methods for recoding the Brain Signals of the Brain?

Dr. Rishabh Pathak
The various methods for recording brain signals in detail, focusing on both non-invasive and invasive techniques.  1. Electroencephalography (EEG) Type : Non-invasive Description : EEG involves placing electrodes on the scalp to capture electrical activity generated by neurons. It records voltage fluctuations resulting from ionic current flows within the neurons of the brain. This method provides high temporal resolution (millisecond scale), allowing for the monitoring of rapid changes in brain activity. Advantages : Relatively low cost and easy to set up. Portable, making it suitable for various applications, including clinical and research settings. Disadvantages : Lacks spatial resolution; it cannot precisely locate where the brain activity originates, often leading to ambiguous results. Signals may be contaminated by artifacts like muscle activity and electrical noise. Developments : ...
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