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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...
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Judgement Sampling

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Dr. Rishabh Pathak

Judgment sampling, also known as purposive or selective sampling, is a non-probability sampling technique where researchers use their judgment and expertise to select sample units based on specific criteria or characteristics relevant to the research objectives. In judgment sampling, researchers intentionally choose sample units that they believe are representative or typical of the population of interest. Here are some key points about judgment sampling:


1.    Definition:

§  Judgment sampling is a non-probability sampling method where researchers select sample units based on their judgment, expertise, or knowledge of the population.

§  Sample units are chosen deliberately to represent certain traits, characteristics, or experiences that are deemed relevant to the research objectives.

2.    Characteristics:

§  Judgment sampling relies on the researcher's subjective judgment and understanding of the population to select sample units that are considered typical, informative, or representative.

§  Researchers may use their expertise to identify key characteristics or criteria for selecting sample units that align with the research focus.

3.    Types of Judgment Sampling:

§  Convenience Sampling: Selecting sample units based on their accessibility, availability, or convenience to the researcher.

§  Expert Sampling: Choosing sample units based on the expertise, knowledge, or qualifications of the individuals selected.

§  Typical Case Sampling: Selecting sample units that are considered typical or illustrative of the population's characteristics or behaviors.

4.    Advantages:

§  Judgment sampling allows researchers to focus on specific characteristics or traits of interest, making it suitable for targeted research objectives or exploratory studies.

§  This method is valuable for qualitative research, case studies, and situations where in-depth insights or unique perspectives are sought.

5.    Limitations:

§  Results obtained from judgment samples may be subject to bias, as the selection of sample units is based on the researcher's subjective judgment rather than randomization.

§  The generalizability of findings from judgment sampling may be limited, as the sample may not be representative of the entire population.

6.    Applications:

§  Judgment sampling is commonly used in qualitative research, ethnographic studies, and exploratory research where researchers seek to understand specific phenomena or behaviors.

§  This method is particularly useful when studying unique populations, rare events, or complex phenomena that require expert judgment in sample selection.

7.    Considerations:

§  Researchers should clearly define the criteria for selecting sample units in judgment sampling and justify their choices based on the research objectives.

§  While judgment sampling offers flexibility and targeted sampling, researchers should acknowledge its limitations in terms of generalizability and potential bias.

Judgment sampling is a valuable sampling technique that allows researchers to strategically select sample units based on specific criteria or characteristics relevant to their research goals. While this method offers advantages in terms of targeted sampling and in-depth exploration, researchers should be mindful of its limitations in terms of representativeness and potential bias. Careful consideration of the research objectives and criteria for sample selection is essential when employing judgment sampling in a study.

 

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