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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...
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Decision Trees

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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 splitting subsets until a stopping criterion is met (e.g., maximum depth, minimum samples per leaf).

Example analogy from the book:

·         To distinguish animals like bears, hawks, penguins, and dolphins, decision trees ask questions like “Does the animal have feathers?” to split the dataset into smaller groups, continuing with further specific questions.

·         Such questions form a tree structure where navigating from the root to a leaf corresponds to a series of questions and answers, leading to a classification decision,.

3. Advantages of Decision Trees

  • Easy to understand and visualize: The flow of decisions can be depicted as a tree, which is interpretable even for non-experts (especially for small trees).
  • No need for feature scaling: Decision trees are invariant to scaling or normalization since splits are based on thresholds on feature values and not on distances.
  • Handles both numerical and categorical data: Trees can work with a mix of continuous, ordinal, and categorical features without special preprocessing.
  • Automatic feature selection: Only relevant features are used for splits, providing a form of feature selection.

4. Weaknesses of Decision Trees

  • Tendency to overfit: Decision trees can create very complex trees fitting the noise in training data, leading to poor generalization performance.
  • Unstable: Small variations in data can lead to very different trees.
  • Greedy splits: Recursive partitioning is greedy and locally optimal but not guaranteed to find the best overall tree.

Due to these issues, single decision trees are often outperformed by ensemble methods like random forests and gradient-boosted trees,.

5. Parameters and Tuning

Key parameters controlling decision tree construction:

  • max_depth: Maximum depth of the tree. Limiting depth controls overfitting.
  • min_samples_split: Minimum number of samples required to split a node.
  • min_samples_leaf: Minimum number of samples required to be at a leaf node.
  • max_features: The number of features to consider when looking for the best split.
  • criterion: The function to measure split quality, e.g. "gini" or "entropy" for classification, "mse" for regression.

Proper tuning of these parameters helps optimize the balance between underfitting and overfitting.

6. Extensions: Ensembles of Decision Trees

To overcome the limitations of single trees, ensemble methods combine multiple trees for better performance and stability:

  • Random Forests: Build many decision trees on bootstrap samples of data and average the results, injecting randomness by limiting features for splits to reduce overfitting.
  • Gradient Boosted Decision Trees: Sequentially build trees that correct errors of previous ones, resulting in often more accurate but slower-to-train models.

Both approaches maintain some advantages of trees (e.g., no need for scaling, interpretability of base learners) while significantly enhancing performance.

7. Visualization of Decision Trees

  • Because the model structure corresponds directly to human-understandable decisions, decision trees can be visualized as flowcharts.
  • Visualization aids in understanding model decisions and debugging.

8. Summary

Aspect

Description

Model Type

Hierarchical if/else decision rules forming a tree

Tasks

Classification and regression

Strengths

Interpretable, no scaling needed, handles mixed data

Weaknesses

Prone to overfitting, unstable with small changes

Key Parameters

max_depth, min_samples_split, criterion, max_features

Use in Ensembles

Building block for robust models like Random Forests and Gradient Boosted Trees
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