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

Sensitive of surface morphology with respect to Stiffness Ratio

Image
Dr. Rishabh Pathak

The sensitivity of surface morphology with respect to the stiffness ratio between the cortex and subcortex is a crucial factor in understanding the mechanisms of cortical folding and brain development. Here are some key points regarding the sensitivity of surface morphology to the stiffness ratio:


1.  Influence on Folding Patterns: The stiffness ratio between the cortex and subcortex plays a significant role in shaping the folding patterns of the cerebral cortex. Variations in the stiffness ratio can lead to changes in the depth, frequency, and complexity of cortical folds, impacting the overall surface morphology of the brain.


2.  Stress Distribution: Differences in stiffness between the cortex and subcortex affect the distribution of mechanical stresses within the brain tissue. A mismatch in stiffness can result in uneven stress distribution, leading to alterations in cortical folding patterns and surface morphology.


3.     Surface Deformations: Changes in the stiffness ratio can influence the extent of surface deformations and the formation of cortical folds. A higher stiffness ratio may promote smoother brain surfaces with shallower folds, while a lower stiffness ratio can lead to more pronounced folding patterns.


4.     Mechanical Stability: The stiffness ratio contributes to the mechanical stability of the brain tissue and its ability to resist deformations. An optimal balance in stiffness between the cortex and subcortex is essential for maintaining structural integrity and preventing excessive folding or stretching of the cortical surface.


5.     Computational Modeling: Computational models can simulate the sensitivity of surface morphology to variations in the stiffness ratio by adjusting this parameter and observing the resulting changes in cortical folding patterns. These models provide insights into how the stiffness ratio influences the mechanical behavior and morphological features of the brain.


6.     Clinical Relevance: Abnormalities in the stiffness ratio between cortical layers have been associated with neurodevelopmental disorders and brain pathologies. Understanding the impact of the stiffness ratio on surface morphology can provide valuable insights into the underlying mechanisms of these conditions.


7. Biomechanical Interactions: The stiffness ratio is part of the complex biomechanical interactions that govern cortical folding and brain development. It interacts with other factors such as cortical thickness, growth rates, and genetic influences to shape the structural and functional properties of the cerebral cortex.


By investigating the sensitivity of surface morphology to the stiffness ratio, researchers can gain a deeper understanding of the mechanical principles underlying cortical folding and brain morphogenesis. This knowledge is essential for elucidating the intricate processes that govern brain development and for exploring the implications of mechanical factors in neurodevelopmental disorders and brain health.

 

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

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

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

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

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