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

Neuro-Computational Model of Subcortical Growth

Image
Dr. Rishabh Pathak

A neuro-computational model of subcortical growth integrates principles from neuroscience and computational modeling to study the development of brain regions beneath the cerebral cortex, known as the subcortex. Here are the key aspects of a neuro-computational model of subcortical growth:


1. Biologically Realistic Representation: The model incorporates biologically relevant features of subcortical development, such as the growth and elongation of axons, the formation of neural circuits, and the influence of growth factors on subcortical structures. By simulating these processes computationally, researchers can study how subcortical regions develop and interact with the cortex.


2.     Axonal Growth and Connectivity: The model accounts for the growth of axons and the establishment of connections between subcortical regions and cortical areas. By simulating axonal elongation and branching, researchers can study how subcortical structures contribute to the overall connectivity and function of the brain.


3. Mechanical Interactions: The model considers the mechanical interactions between the subcortex and the overlying cortex, as well as the effects of growth-induced deformations on subcortical structures. By incorporating mechanical properties and growth-induced stresses, the model can investigate how mechanical forces influence subcortical growth patterns.


4.  Stretch-Induced Growth: The model includes mechanisms of stretch-induced growth, where chronic stretching of axons in the subcortex leads to gradual elongation and deformation. By simulating how axons respond to mechanical stimuli, researchers can study the effects of stretch-induced growth on subcortical morphology.


5. Computational Simulations: Neuro-computational models use computational simulations, such as finite element analysis or agent-based models, to study the dynamics of subcortical growth. These simulations allow researchers to investigate how interactions between neurons, glial cells, and mechanical forces shape the development of subcortical structures.


6.  Sensitivity Analysis: The model can perform sensitivity analyses to assess the impact of varying parameters, such as growth rates, mechanical properties, and external stimuli, on subcortical growth. By systematically varying these parameters in simulations, researchers can identify key factors influencing the morphogenesis of subcortical regions.


7.    Validation and Comparison: Neuro-computational models are validated against experimental data, such as neuroimaging studies or histological analyses, to ensure their biological accuracy. By comparing model predictions with empirical observations, researchers can evaluate the model's ability to capture the dynamics of subcortical growth.


8.  Insights into Brain Development: By studying subcortical growth processes computationally, researchers can gain insights into the mechanisms underlying the development of brain structures below the cortex. These models help elucidate how subcortical regions contribute to overall brain function and connectivity, providing a deeper understanding of brain development. 


In summary, a neuro-computational model of subcortical growth offers a valuable framework for investigating the complex processes involved in the development of brain regions beneath the cerebral cortex. By combining neuroscience principles with computational modeling techniques, researchers can explore the dynamics of subcortical growth, connectivity formation, and mechanical interactions within the developing brain.

 

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

What is Quantitative growth of the human brain?

Dr. Rishabh Pathak
Quantitative growth of the human brain involves the detailed measurement and analysis of various physical and biochemical parameters to understand the developmental changes that occur in the brain over time. Researchers quantify aspects such as brain weight, DNA content, cholesterol levels, water content, and other relevant factors in different regions of the brain at various stages of development, from prenatal to postnatal years.      By quantitatively assessing these parameters, researchers can track the growth trajectories of the human brain, identify critical periods of rapid growth (such as growth spurts), and compare these patterns across different age groups and brain regions. This quantitative approach provides valuable insights into the structural and biochemical changes that underlie brain development, allowing for a better understanding of normal developmental processes and potential deviations from typical growth patterns.      Furthermore,...
Post a Comment
Read more