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Neurons can code for multiple variables simultaneously and neuroscientistsare often interested in classifying neurons based on their receptive fieldproperties. Statistical models provide powerful tools for determining thefactors influencing neural spiking activity and classifying individual neurons.However, as neural recording technologies have advanced to produce simultaneousspiking data from massive populations, classical statistical methods often lackthe 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, alongwith accurate labels to fit well. Additionally, model assessment andinterpretation are often more challenging for ML than for classical statisticalmethods. To address these challenges, we develop an integrated framework,combining statistical modeling and machine learning approaches to identify thecoding properties of neurons from large populations. In order to demonstratethis framework, we apply these methods to data from a population of neuronsrecorded from rat hippocampus to characterize the distribution of spatialreceptive fields in this region.

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