Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populations
This work addresses the problem of inefficient and interpretable analysis of massive neural recordings for neuroscientists, representing an incremental improvement by hybridizing existing methods.
The authors tackled the challenge of analyzing large-scale neural population data by developing an integrated framework that combines statistical modeling and machine learning approaches to identify neuron receptive field structures, applying it to rat hippocampus data to characterize spatial receptive field distributions.
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.