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

Mechanisms of Brain Development

Image
Dr. Rishabh Pathak

Brain development is a complex and highly orchestrated process involving a series of intricate mechanisms that shape the structure and function of the brain. Here are some key mechanisms of brain development:


1.     Neurogenesis: Neurogenesis is the process by which neurons are generated from neural stem cells. It occurs predominantly during embryonic development but continues in certain brain regions throughout life. Neurogenesis is essential for the formation of the brain's neuronal circuitry.


2. Cell Migration: Once neurons are generated, they must migrate to their appropriate locations within the brain to form functional circuits. Cell migration is crucial for establishing the correct connectivity and organization of the brain.


3.  Axon Guidance: Axon guidance is the process by which developing axons navigate to their target regions and establish connections with other neurons. Guidance cues, such as growth factors and cell adhesion molecules, play a critical role in directing axon growth and pathfinding.


4.  Synaptogenesis: Synaptogenesis is the formation of synapses, the connections between neurons that allow for communication in the brain. Synaptic connections are essential for neural signaling and the establishment of functional circuits.


5. Apoptosis: Apoptosis, or programmed cell death, is a natural process that eliminates excess neurons and synapses during brain development. Apoptosis helps sculpt the developing brain by removing unnecessary or improperly connected cells.


6.  Myelination: Myelination is the process by which axons are insulated with myelin, a fatty substance that enhances the speed and efficiency of neural signaling. Myelination occurs throughout development and is essential for proper brain function.


7.     Experience-Dependent Plasticity: Experience-dependent plasticity refers to the brain's ability to reorganize and adapt in response to sensory experiences and environmental stimuli. This mechanism plays a crucial role in shaping neural circuits and optimizing brain function.


8. Critical Periods: Critical periods are specific windows of time during development when the brain is particularly sensitive to certain types of stimuli. During these periods, neural circuits are more malleable and can be shaped by sensory experiences.


9.  Hormonal Influence: Hormones play a significant role in brain development, influencing processes such as cell proliferation, differentiation, and synaptic connectivity. Hormonal signals help coordinate various aspects of brain development.


10. Genetic Regulation: Genetic factors play a fundamental role in brain development, influencing the formation of neural structures, cell types, and connectivity patterns. Genetic regulation guides the intricate processes of brain development from the molecular level up to the macroscopic organization of the brain.


Understanding these mechanisms of brain development is essential for unraveling the complexities of how the brain forms and functions. Researchers continue to investigate these processes to gain insights into neurodevelopmental disorders, brain plasticity, and the fundamental principles underlying brain structure and function.

 

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

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

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

Uncertainty Estimates from Classifiers

Dr. Rishabh Pathak
1. Overview of Uncertainty Estimates Many classifiers do more than just output a predicted class label; they also provide a measure of confidence or uncertainty in their predictions. These uncertainty estimates help understand how sure the model is about its decision , which is crucial in real-world applications where different types of errors have different consequences (e.g., medical diagnosis). 2. Why Uncertainty Matters Predictions are often thresholded to produce class labels, but this process discards the underlying probability or decision value. Knowing how confident a classifier is can: Improve decision-making by allowing deferral in uncertain cases. Aid in calibrating models. Help in evaluating the risk associated with predictions. Example: In medical testing, a false negative (missing a disease) can be worse than a false positive (extra test). 3. Methods to Obtain Uncertainty from Classifiers 3.1 ...
Post a Comment
Read more