Evaluating place cell detection methods in Rats and Humans: Implications for cross-species spatial coding
Description
Place cells, first identified in the rat hippocampus as neurons that fire selectively at specific locations, are central to investigations of the neural underpinnings of spatial navigation. Recent spatial studies in human patients with drug-resistant epilepsy have made identifying and characterizing place cells across species increasingly important for understanding the extent to which decades of rodent research generalize to humans and for uncovering fundamental principles of spatial cognition. One challenge, however, is that detection methods differ: rodent studies often rely on spatial information (SI) in conjunction with place field stability measures, whereas human studies employ analysis of variance (ANOVA) based approaches. These methodological differences may affect the identified place cell populations, which complicates how their properties are interpreted and cross-species comparisons. To address this, we systematically applied multiple detection pipelines to human and rat datasets, supported by simulations that vary place-field properties. Our analyses and simulations demonstrate that spatial information and ANOVA-based approaches are responsive to distinct place field properties: spatial information primarily reflects the contrast between peak and average firing rates, while ANOVA emphasizes consistency across trials. Across species, rodent place cells revealed a broad spectrum of spatial tuning, including strongly tuned neurons with high spatial information and high ANOVA values. In contrast, human place cells lacked this strongly tuned population and exhibited a narrower distribution of tuning scores, concentrated at the lower end of both spatial tuning metrics. Despite these differences, both species had an overlapping population of neurons with weaker yet consistent spatial tuning, which may support important functional roles such as generalization and mixed selectivity. Addressing these analytical differences allows for more direct comparisons between species, though differences in spatial tuning may still relate to variations in experimental paradigms that warrant further investigation. Together, our study provides a roadmap showing how spatial tuning metrics shape place cell detection and interpretation.
Data availability
This project is openly available through an online project repository, which includes all the code used for data pre-processing and analysis. Project Repository: https://github.com/HSNPipeline/PlaceCellMethods Dataset This project uses electrophysiological data collected from neurosurgical patients, as well as an open access rat recording dataset from CRCNS.org: http://dx.doi.org/10.6080/K09G5JRZ. The human single-neuron dataset was collected as part of a previously published study and is publicly available through OSF [14]: https://osf.io/dh3wv/metadata/osf. To systematically evaluate place cell detection methods across species, we developed a custom simulation framework, SimPlaceCells, available at: https://github.com/HSNPipeline/SimPlaceCells. Software All code used and developed for this project was written in the Python programming language. The code is openly available, licensed for reuse, and deposited in the project repository. Management of the dataset was conducted using the Human Single Neuron (HSN) Pipeline: https://github.com/HSNPipeline Analyses of the single-neuron data were performed using the open-source Spiketools toolbox: https://github.com/spiketools/spiketools Literature searches and related resources were organized using LISC, an open-source Python module for literature analysis. https://github.com/lisc-tools/lisc.
Files
journal.pcbi.1013488.pdf
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Additional details
Funding
- National Institutes of Health
- R01MH104606