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

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

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

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

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

Computational Model

Dr. Rishabh Pathak
A computational model in the context of brain development refers to a mathematical and numerical representation of the processes involved in the growth and morphogenesis of the brain. Here are the key aspects of a computational model in the study of brain development: 1.    Numerical Simulation : A computational model allows researchers to simulate and analyze the complex processes of brain development using numerical methods. By translating biological principles and mechanical behaviors into mathematical equations, researchers can simulate the growth and deformation of brain structures over time. 2.    Finite Element Analysis : Computational models often utilize finite element analysis, a numerical technique for solving partial differential equations, to simulate the mechanical behavior of brain tissue during growth. This method enables researchers to predict how the brain's structure changes in response to growth-induced stresses and strains. 3.    Parame...
Post a Comment
Read more