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

A Model of Prefrontal Cortex Functions

Image
Dr. Rishabh Pathak

A comprehensive model of prefrontal cortex (PFC) functions integrates various cognitive processes and neural mechanisms associated with executive function, cognitive control, decision-making, and emotional regulation. Here is an overview of a model that captures the complexity of PFC functions:


1.     Thalamus and Amygdala:

o  Quick Emotional Responses: The model posits that the thalamus and amygdala generate rapid emotional response tendencies in reaction to stimuli.

2.     Orbitofrontal Cortex:

o    Evaluation and Reward Processing: The orbitofrontal cortex receives input from the thalamus and amygdala and is involved in evaluating the emotional and motivational significance of stimuli. It generates simple approach-avoidance rules based on emotional valence and is crucial for learning to reverse these rules in response to changing contexts.

3.     Anterior Cingulate Cortex:

o Performance Monitoring: The anterior cingulate cortex acts as a performance monitor, signaling the need for higher-level processing in the lateral PFC when the initial response is inadequate. It is involved in error detection, conflict monitoring, and adjusting cognitive control based on task demands.

4.     Lateral Prefrontal Cortex:

o    Reprocessing and Rule Representation:

§  Ventrolateral PFC and Dorsolateral PFC: These regions are involved in reprocessing information and representing rules at different levels of complexity. They support the maintenance of task sets, working memory, and cognitive flexibility.

§ Rostrolateral PFC: This region is responsible for explicit consideration of task sets and coordinating complex cognitive operations. It integrates information from multiple sources and supports strategic decision-making.

5.     Information Processing:

o  The model emphasizes the hierarchical organization of the PFC, with different regions contributing to distinct aspects of cognitive control, decision-making, and goal-directed behavior.

o    The PFC integrates emotional, motivational, and cognitive information to guide adaptive responses and regulate behavior in dynamic environments.

6.     Iterative Reprocessing:

o    The model suggests that information processing in the PFC involves iterative reprocessing of stimuli at multiple levels of complexity, from basic emotional responses to higher-order cognitive rules and strategies.

o  This iterative reprocessing allows for the flexible adaptation of behavior based on changing internal and external demands, supporting adaptive decision-making and goal pursuit.

By incorporating the roles of different PFC regions in emotional evaluation, cognitive control, and rule representation, this model provides a framework for understanding the neural mechanisms underlying executive function and adaptive behavior mediated by the prefrontal cortex.

 

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

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

Conducting a Qualitative Analysis

Dr. Rishabh Pathak
Conducting a qualitative analysis in biomechanics involves a systematic process of collecting, analyzing, and interpreting non-numerical data to gain insights into human movement patterns, behaviors, and interactions. Here are the key steps involved in conducting a qualitative analysis in biomechanics: 1.     Data Collection : o     Use appropriate data collection methods such as video recordings, observational notes, interviews, or focus groups to capture qualitative information about human movement. o     Ensure that data collection is conducted in a systematic and consistent manner to gather rich and detailed insights. 2.     Data Organization : o     Organize the collected qualitative data systematically, such as transcribing interviews, categorizing observational notes, or indexing video recordings for easy reference during analysis. o     Use qualitative data management tools or software to f...
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