Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populations

TL;DR

This paper presents an integrated framework combining statistical and machine learning methods to efficiently classify neural receptive fields in large populations, demonstrated on rat hippocampus data.

Contribution

The study introduces a novel combined approach that leverages the strengths of both statistical models and machine learning for neural classification tasks.

Findings

Effective classification of neural receptive fields in large datasets

Demonstrated framework on rat hippocampus data

Improved interpretability and efficiency over traditional methods

Abstract

Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as neural recording technologies have advanced to produce simultaneous spiking data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analyses; however, they typically require massive training sets with balanced data, along with accurate labels to fit well. Additionally, model assessment and interpretation are often more challenging for ML than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to demonstrate this framework, we apply these methods to data from a population of neurons recorded from rat hippocampus to characterize the distribution of spatial receptive fields in this region.