P078 Single neuron representational drift in CA1 can be explained by aligning stable low dimensional manifolds
Elena Faillace*1, Mary Ann Go1, Juan Álvaro Gallego1, Simon Schultz1
1Centre for Neurotechnology and Department of Bioengineering, Imperial College London, UK
*Email: elena.faillace20@imperial.ac.uk
Hippocampal place cells are believed to form a cognitive map that supports spatial navigation. However, their spatial tuning has been observed to ‘remap’, i.e. the representation drifts over time, even in the same environment [1]. This raises the question of how a robust and consistent experience is maintained despite continual remapping at the single cell level. Furthermore, it remains unclear whether this drift is coordinated across neurons, and how tuning curves profiles evolve. Here, we propose a population-level approach to identify a stable representation of environments and provide a framework to predict the activity of remapped tuning curves.
We performed two-photon calcium imaging to record the activity of hundreds of neurons in CA1 of head-fixed mice during a running task (Fig.a,b) [2]. Mice expressing GCaMP6s were habituated to a circular track for 7-9 days, followed by 3 days of recordings. All environments had the same circular structure but differed in the visual cues along the walls. During imaging, mice were exposed to two familiar environments, one novel environment, and one familiar environment with inverted order of the symbols on the walls. Neurons were longitudinally registered across sessions using CaImAn.
We used linear dimensionality reduction techniquesto find session-specific manifolds that spanned the coordinated activity of CA1 cells (Fig.c). Using a combination of PCA and canonical correlation analysis (CCA)[3,4], we were able to align these session-specific manifolds (Fig.d) across days, environments, and even mice, achieving robust decoding of the animal's position along the track (Fig.h). Moreover, using this aligned manifold, we could predict the remapping of single neuron tuning curves (Fig.e,f,g), even for those excluded when computing the alignment procedure.
This work supports the perspective that neural manifolds serve as a stable basis for neural encoding [3,4]. We present a framework in which representational drift, traditionally viewed as unstructured, can be interpreted as a coordinated adaptation at a population level, enabling the prediction of tuning curve profiles for ‘unseen’ neurons. Importantly, we did not need to categorise or select neurons based on their functional classes (e.g., place cells), thereby acknowledging their collective contribution to a preserved manifold space.
Figure 1. (a,b): schematic of experiment set-up and Ca+ imaging. Previously presented in [2]. (c): PCA of the average firing rates from different sessions concatenated. d. PCA space after each recording has been projected to a common PC space (alignment). (e,f): example of tuning curves pre and after alignment and their correlation and L2 norm (g). (h): Same as (d), colour coded by angular position. Acknowledgements
