bioRxiv2025
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. With recent work with human patients, identifying and characterizing place cells across species has become increasingly important for understanding the extent to which decades of rodent research generalize to humans and uncovering principles of spatial cognition. One challenge, however, is that detection methods differ: rodent studies often rely on spatial information (SI), whereas human studies employ analysis of variance (ANOVA) - based approaches. These methodological differences may affect the identified place cell population, 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 (SI) 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. Together, our study provides a roadmap showing how spatial tuning metrics shape place cell detection and interpretation, while underscoring the functional importance of weaker-tuned neurons in cross-species comparisons. Author SummaryPlace cells are neurons that become active in specific locations, and they play a critical role in how the brain supports navigation and memory. Place cells were first discovered in rats and later observed in humans, however, there has been a lack of direct comparisons between species using comparable approaches. Part of the difficulty doing so is that studies of rodent and human place cells have often relied on different analysis methods, making it difficult to determine if and how place-cell properties differ between species. To address this, in this study, we set out to understand how differences in place cell detection methods affect the identified place cell populations and interpretations of spatial coding across species. To do so, we compared the most prevalent detection methods used in rodent and human research side by side, applying them to datasets from both species and to simulations. We found that different methods emphasize different features of spatial responses, which changes which neurons are identified as place cells. Across species, rat recordings revealed a wide range of spatial responses, from neurons with sharply localized activity to those with broader but reliable patterns. Human recordings, by contrast, were more concentrated at weaker but consistent levels of tuning. Importantly, these weaker but consistent responses reflect an overlapping population of neurons found in both species, which may serve similar functional roles in supporting flexible spatial memory and generalization. By separating methodological effects from biological differences, we lay the groundwork for future cross-species studies for spatial coding. Materials Descriptions and Availability StatementsO_ST_ABSProject RepositoryC_ST_ABSThis 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/HSUPipeline/PlaceCellMethods DatasetThis project uses electrophysiological data collected from neurosurgical patients, as well as an open-access dataset of rat recordings from CRCNS.org: http://dx.doi.org/10.6080/K09G5JRZ The human data were collected as part of a previously published study and will be made available prior to publication [1]. A custom simulation framework was developed to evaluate place cell detection methods across species and will be released as part of the open-source SpikeTools repository prior to publication. SoftwareAll 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 Unit (HSU) Pipeline: https://github.com/HSUPipeline 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/HSUPipeline/Literature