Vladislav Ivantaev: Place cells support stability of 2d-place decoding from hippocampal CA1
| When |
Dec 10, 2024
from 05:15 PM to 05:45 PM |
|---|---|
| Where | Bernstein Center, Lecture Hall, ground floor, Hansastr. 9a |
| Contact Name | Gundel Jaeger |
| Contact Phone | 0761 203 9549 |
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Abstract
The hippocampus is thought to be involved in spatial navigation, by constructing a cognitive map of space consisting of place cells [1] (PCs). Moreover, the hippocampus is crucial for the formation of memories, however, it is unclear whether and how PC activity relates to memory: turnover of neural representations could be involved in learning, while stability may support long-term storage and retrieval of memories [2].
In this work, we focus on analyzing one-photon (1P) calcium imaging data recorded from mouse CA1 during free foraging in a two-dimensional arena with the help of a miniature microscope [3]. Ca2+ transients, extracted with the help of CaImAn software [4], are used to compute place fields and spatial information [5] (SI) values.
Assuming Poisson statistics of spike trains we identified statistically significant PCs, showing that (18.8±13.2) % of cells have significant place fields, which is higher than the fraction yielded in a similar previous study [6]. However, the median SI value of 0.39±0.08 is much lower than typically observed in electrophysiological recordings. In addition, Support Vector Regression [7] (SVR) was used to decode running trajectories providing errors less than 20% of the arena diameter. Place cells and non-place cells thereby contributed to decoding equally. However, place cells were more important when decoders were applied to recordings made on different days. Also, this finding confirms further analysis showing higher stability of place cell activity as compared to non-place cell activity across days.
[1] John O’Keefe and Lynn Nadel. The Hippocampus as a Cognitive Map. 1978
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[3] Chenani, A., Weston, G., Ulivi, A.F. et al. Repeated stress exposure leads to structural synaptic instability prior to disorganization of hippocampal coding and impairments in learning. Transl Psychiatry 12, 381 (2022). https://doi.org/10.1038/s41398-022-02107-5
[4] Andrea Giovannucci, Johannes Friedrich, Pat Gunn, Jérémie Kalfon, Brandon L Brown, Sue Ann Koay, Jiannis Taxidis, Farzaneh Najafi, Jeffrey L Gauthier, Pengcheng Zhou, Baljit S Khakh, David W Tank, Dmitri B Chklovskii, and Eftychios A Pnevmatikakis. “CaImAn an open source tool for scalable calcium imaging data analysis”. In: eLife 8 (2019), pp. 1–45.
[5] William E. Skaggs and Bruce L.McNaughton and Katalin M. Gothard and Etan J. Markus. “An Information-Theoretic Approach to Deciphering the Hippocampal Code”. In: Proceedings of the IEEE 1990 (1993), pp. 1030–1037.
[6] Fabio Stefanini, Lyudmila Kushnir, Jessica C. Jimenez, Joshua H. Jennings, Nicholas I. Woods, Garret D. Stuber, Mazen A. Kheirbek, René Hen, Stefano Fusi, “A Distributed Neural Code in the Dentate Gyrus and in CA1”, Neuron, Volume 107, Issue 4, 2020, ISSN 0896-6273, https://doi.org/10.1016/j.neuron.2020.05.022.
[7] John C. Platt. Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. 1999