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

The Means of obtaining information

Image
Dr. Rishabh Pathak

The means of obtaining information in a research study refer to the methods, techniques, and tools used to collect data and gather relevant information for the research project. Here are some common means of obtaining information in research:


1.    Surveys:

o    Surveys involve collecting data from a sample of individuals or respondents through structured questionnaires or interviews. Surveys can be conducted in person, over the phone, through mail, or online, and they are commonly used to gather information on attitudes, opinions, behaviors, and demographics.

2.    Interviews:

o    Interviews involve direct interaction between the researcher and the participant to gather in-depth information, insights, and perspectives on the research topic. Interviews can be structured, semi-structured, or unstructured, depending on the level of standardization and flexibility needed in data collection.

3.    Observations:

o Observations involve systematically watching and recording behaviors, events, or phenomena in their natural settings. Observational methods can provide valuable qualitative data and insights into real-life behaviors and interactions without relying on self-reporting or participant responses.

4.    Experiments:

o    Experiments involve manipulating variables and conditions to test causal relationships and hypotheses. Experimental research allows researchers to control and manipulate independent variables to observe their effects on dependent variables, providing insights into cause-and-effect relationships.

5.    Secondary Data Analysis:

o    Secondary data analysis involves using existing data sources, such as published studies, reports, databases, and archives, to answer research questions or test hypotheses. Researchers analyze and interpret secondary data to generate new insights or validate findings from primary research.

6.    Focus Groups:

o Focus groups involve bringing together a small group of participants to discuss specific topics, issues, or products in a guided discussion format. Focus groups are used to gather qualitative data, explore opinions, perceptions, and attitudes, and generate insights through group interactions.

7.    Document Analysis:

o    Document analysis involves reviewing and analyzing written, visual, or audio-visual materials, such as texts, reports, articles, images, videos, or archival records. Researchers examine documents to extract information, identify patterns, and gain insights into historical, cultural, or textual contexts.

8.    Case Studies:

o    Case studies involve in-depth investigation of a single individual, group, organization, or phenomenon to understand complex issues, contexts, or processes. Case studies use multiple sources of data, such as interviews, observations, documents, and artifacts, to provide detailed and rich descriptions.

9.    Ethnographic Research:

o Ethnographic research involves immersive fieldwork and participant observation in natural settings to study cultures, communities, or social phenomena. Ethnographers engage with participants, observe behaviors, and document cultural practices to gain deep insights into social contexts.

10.Content Analysis:

o    Content analysis involves systematically analyzing and interpreting the content of texts, media, or communication materials to identify themes, patterns, or trends. Researchers use content analysis to quantify and analyze textual data, such as news articles, social media posts, or speeches.

These means of obtaining information offer researchers a variety of tools and techniques to collect data, gather insights, and generate knowledge in different research contexts and disciplines. Researchers select and combine these methods based on the research objectives, research questions, data requirements, and the nature of the research problem to ensure the validity, reliability, and relevance of the information obtained for the study.

 

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

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

Neural Networks in Machine Learning

Dr. Rishabh Pathak
1. Introduction to Neural Networks Neural networks are a family of models inspired by the biological neural networks in the brain. They consist of layers of interconnected nodes ("neurons"), which transform input data through a series of nonlinear operations to produce outputs. Neural networks are versatile and can model complex patterns and relationships, making them foundational in modern machine learning and deep learning. 2. Basic Structure: Multilayer Perceptrons (MLPs) The simplest neural networks are Multilayer Perceptrons (MLPs) , also called vanilla feed-forward neural networks . MLPs consist of: Input layer : Receives features. Hidden layers : One or more layers that perform nonlinear transformations. Output layer : Produces the final prediction (classification or regression). Each neuron in one layer connects to every neuron in the next layer via weighted links. Computation progresses f...
Post a Comment
Read more