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

Procedures and techniques to be used for gathering information’s

Image
Dr. Rishabh Pathak

When gathering information for a research study, it is essential to use appropriate procedures and techniques to ensure the reliability, validity, and relevance of the data collected. Here are some common procedures and techniques used for gathering information in research:


1.    Literature Review:

o   Conduct a comprehensive review of existing literature, research studies, and scholarly sources related to the research topic. Use academic databases, journals, books, and reputable sources to gather background information, theoretical frameworks, and previous findings relevant to the study.

2.    Surveys:

o   Design and administer surveys to collect data from a sample of respondents. Use structured questionnaires with closed-ended or open-ended questions to gather quantitative or qualitative data. Consider online surveys, paper-based surveys, face-to-face interviews, or telephone surveys based on the target population and research objectives.

3.    Interviews:

o  Conduct structured, semi-structured, or unstructured interviews with individuals or groups to gather in-depth insights, opinions, and perspectives on the research topic. Use interview guides, probes, and follow-up questions to explore themes, experiences, and attitudes. Consider face-to-face interviews, phone interviews, or focus group discussions.

4.    Observations:

o  Engage in direct observations of people, events, behaviors, or phenomena to collect firsthand data. Use structured observation protocols, checklists, or field notes to document observations systematically. Consider participant observation, non-participant observation, naturalistic observation, or controlled observation based on the research context.

5.    Experiments:

o  Design and conduct controlled experiments to test hypotheses, manipulate variables, and establish causal relationships. Use experimental designs, randomization, control groups, and treatment conditions to collect quantitative data. Consider laboratory experiments, field experiments, quasi-experiments, or randomized controlled trials based on the research objectives.

6.    Document Analysis:

o  Analyze documents, records, archives, reports, or artifacts to extract data and information relevant to the research study. Use content analysis, document coding, and thematic analysis to identify patterns, themes, and trends in textual or visual materials. Consider historical documents, policy documents, organizational reports, or public records for analysis.

7.    Focus Groups:

o  Organize focus group discussions with a small group of participants to explore opinions, attitudes, and perceptions on specific topics. Use a moderator guide, group dynamics, and interactive discussions to generate qualitative data. Consider diverse participant backgrounds, group interactions, and thematic analysis of focus group data.

8.    Secondary Data Analysis:

o    Utilize existing data sources, datasets, surveys, or databases to analyze secondary data for research purposes. Access public data repositories, government statistics, academic archives, or organizational records to conduct secondary data analysis. Consider data cleaning, data transformation, and data merging techniques for secondary data analysis.

9.    Ethnography:

o    Engage in ethnographic research to immerse in a cultural setting, community, or social group to understand behaviors, practices, and norms. Use participant observation, field notes, interviews, and cultural immersion techniques to collect qualitative data. Consider reflexivity, cultural sensitivity, and insider perspectives in ethnographic research.

10. Mixed Methods:

o  Combine multiple data collection methods, such as surveys, interviews, observations, and document analysis, in a mixed methods research design. Use triangulation, data integration, and methodological pluralism to enhance the depth and breadth of data collected. Consider sequential, concurrent, or transformative mixed methods approaches based on the research questions.

By employing these procedures and techniques for gathering information in research, researchers can collect diverse, reliable, and valid data to address research questions, test hypotheses, and generate meaningful insights for their studies. It is important to select the most appropriate methods based on the research objectives, research design, sample characteristics, ethical considerations, and practical constraints to ensure the quality and rigor of the data collected.

 

Categories:

Comments

Post a Comment

Popular posts from this blog

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

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

Mesencephalic Locomotor Region (MLR)

Dr. Rishabh Pathak
The Mesencephalic Locomotor Region (MLR) is a region in the midbrain that plays a crucial role in the control of locomotion and rhythmic movements. Here is an overview of the MLR and its significance in neuroscience research and motor control: 1.       Location : o The MLR is located in the mesencephalon, specifically in the midbrain tegmentum, near the aqueduct of Sylvius. o   It encompasses a group of neurons that are involved in coordinating and modulating locomotor activity. 2.      Function : o   Control of Locomotion : The MLR is considered a key center for initiating and regulating locomotor movements, including walking, running, and other rhythmic activities. o Rhythmic Movements : Neurons in the MLR are involved in generating and coordinating rhythmic patterns of muscle activity essential for locomotion. o Integration of Sensory Information : The MLR receives inputs from various sensory modalities and higher brain regions t...
Post a Comment
Read more

Seizures

Dr. Rishabh Pathak
Seizures are episodes of abnormal electrical activity in the brain that can lead to a wide range of symptoms, from subtle changes in awareness to convulsions and loss of consciousness. Understanding seizures and their manifestations is crucial for accurate diagnosis and management. Here is a detailed overview of seizures: 1.       Definition : o A seizure is a transient occurrence of signs and/or symptoms due to abnormal, excessive, or synchronous neuronal activity in the brain. o Seizures can present in various forms, including focal (partial) seizures that originate in a specific area of the brain and generalized seizures that involve both hemispheres of the brain simultaneously. 2.      Classification : o Seizures are classified into different types based on their clinical presentation and EEG findings. Common seizure types include focal seizures, generalized seizures, and seizures of unknown onset. o The classification of seizures is esse...
Post a Comment
Read more

Mu Rhythms compared to Ciganek Rhythms

Dr. Rishabh Pathak
The Mu rhythm and Cigánek rhythm are two distinct EEG patterns with unique characteristics that can be compared based on various features.  1.      Location : o     Mu Rhythm : § The Mu rhythm is maximal at the C3 or C4 electrode, with occasional involvement of the Cz electrode. § It is predominantly observed in the central and precentral regions of the brain. o     Cigánek Rhythm : § The Cigánek rhythm is typically located in the central parasagittal region of the brain. § It is more symmetrically distributed compared to the Mu rhythm. 2.    Frequency : o     Mu Rhythm : §   The Mu rhythm typically exhibits a frequency similar to the alpha rhythm, around 10 Hz. §   Frequencies within the range of 7 to 11 Hz are considered normal for the Mu rhythm. o     Cigánek Rhythm : §   The Cigánek rhythm is slower than the Mu rhythm and is typically outside the alpha frequency range. 3. ...
Post a Comment
Read more