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

Quota Sampling

Image
Dr. Rishabh Pathak

Quota sampling is a non-probability sampling technique that involves dividing the population into subgroups or strata based on certain characteristics and then selecting samples from each subgroup in proportion to their presence in the population. Quota sampling is a method of convenience sampling where researchers establish quotas for different subgroups and then non-randomly select participants to fill those quotas. Here are some key points about quota sampling:


1.    Definition:

o Quota sampling is a non-probability sampling method where researchers divide the population into subgroups or strata based on specific characteristics (such as age, gender, income level) and then set quotas for each subgroup.

o    Participants are selected non-randomly to fill the quotas, typically based on convenience or availability, rather than through random selection.

2.    Process:

o    Researchers first identify key characteristics or variables of interest and create quotas to ensure that the sample reflects the diversity of the population.

o    Participants are then selected based on convenience or judgment to meet the predetermined quotas for each subgroup.

3.    Characteristics:

o  Quota sampling allows researchers to ensure that the sample includes representation from different subgroups in the population, making it useful for capturing diversity.

o    This method is often used in situations where random sampling is impractical or costly, but researchers still want to achieve some level of stratification in the sample.

4.    Advantages:

o    Quota sampling provides a structured approach to ensure diversity in the sample by setting quotas for different subgroups.

o    This method can be more efficient and cost-effective than random sampling, especially when specific subgroups need to be represented in the sample.

5.    Limitations:

o    Quota sampling may introduce bias if the selection of participants within each quota is not random or if certain characteristics are overrepresented or underrepresented.

o    Results obtained from quota samples may not be generalizable to the entire population due to the non-random selection process.

6.    Applications:

o   Quota sampling is commonly used in market research, opinion polls, and surveys where researchers want to ensure representation from different demographic groups.

o    This method is suitable for studies that require stratification by specific characteristics but do not require strict randomization.

7.    Considerations:

o    Researchers should carefully define the quotas based on relevant population characteristics and ensure that the selection process within each quota is consistent and transparent.

o    While quota sampling can provide valuable insights into specific subgroups, researchers should be cautious in generalizing findings beyond the sampled population.

Quota sampling offers a practical and structured approach to sampling that allows researchers to ensure diversity and representation from different subgroups in the population. While this method provides advantages in terms of stratification and efficiency, researchers should be aware of its limitations in terms of bias and generalizability. Careful planning and implementation are essential when using quota sampling to ensure the validity and reliability of research findings.

 

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

EEG Amplification

Dr. Rishabh Pathak
EEG amplification, also known as gain or sensitivity, plays a crucial role in EEG recordings by determining the magnitude of electrical signals detected by the electrodes placed on the scalp. Here is a detailed explanation of EEG amplification: 1. Amplification Settings : EEG machines allow for adjustment of the amplification settings, typically measured in microvolts per millimeter (μV/mm). Common sensitivity settings range from 5 to 10 μV/mm, but a wider range of settings may be used depending on the specific requirements of the EEG recording. 2. High-Amplitude Activity : When high-amplitude signals are present in the EEG, such as during epileptiform discharges or artifacts, it may be necessary to compress the vertical display to visualize the full range of each channel within the available space. This compression helps prevent saturation of the signal and ensures that all amplitude levels are visible. 3. Vertical Compression : Increasing the sensitivity value (e.g., from 10 μV/mm to...
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

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