Skip to main content

Posts

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
Read more

Neural Networks in Machine Learning

Dr. Rishabh Pathak
1. Introduction to Neural Networks Neural networks are a family of models inspired by the biological neural networks in the brain. They consist of layers of interconnected nodes ("neurons"), which transform input data through a series of nonlinear operations to produce outputs. Neural networks are versatile and can model complex patterns and relationships, making them foundational in modern machine learning and deep learning. 2. Basic Structure: Multilayer Perceptrons (MLPs) The simplest neural networks are Multilayer Perceptrons (MLPs) , also called vanilla feed-forward neural networks . MLPs consist of: Input layer : Receives features. Hidden layers : One or more layers that perform nonlinear transformations. Output layer : Produces the final prediction (classification or regression). Each neuron in one layer connects to every neuron in the next layer via weighted links. Computation progresses f...
Post a Comment
Read more

Kernelized Support Vector Machines

Dr. Rishabh Pathak
1. Introduction to SVMs Support Vector Machines (SVMs) are supervised learning algorithms primarily used for classification (and regression with SVR). They aim to find the optimal separating hyperplane that maximizes the margin between classes for linearly separable data. Basic (linear) SVMs operate in the original feature space, producing linear decision boundaries. 2. Limitations of Linear SVMs Linear SVMs have limited flexibility as their decision boundaries are hyperplanes. Many real-world problems require more complex, non-linear decision boundaries that linear SVM cannot provide. 3. Kernel Trick: Overcoming Non-linearity To allow non-linear decision boundaries, SVMs exploit the kernel trick . The kernel trick implicitly maps input data into a higher-dimensional feature space where linear separation might be possible, without explicitly performing the costly mapping . How the Kernel Trick Works: Instead of computing ...
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

Decision Trees

Dr. Rishabh Pathak
1. What are Decision Trees? Decision trees are supervised learning models used for classification and regression tasks. They model decisions as a tree structure , where each internal node corresponds to a decision (usually a test on a feature), and each leaf node corresponds to an output label or value. Essentially, the tree learns a hierarchy of if/else questions that partition the input space into regions associated with specific outputs. 2. How Decision Trees Work The model splits the dataset based on feature values in a way that increases the purity of the partitions (i.e., groups that are more homogeneous with respect to the target). At each node, the algorithm evaluates possible splits on features and selects the one that best separates the data, according to a criterion such as Gini impurity , entropy (information gain), or mean squared error (for regression). The process recursively continues sp...
Post a Comment
Read more