Abstract
Hippocampus stores spatial representations, or maps, which are recalled each time a subject is placed in the corresponding environment. Across different environments of similar geometry, these representations show strong orthogonality in CA3 of hippocampus, whereas in the CA1 subfield a considerable overlap between the maps can be seen. The lower orthogonality decreases reliability of various decoders developed in an attempt to identify which of the stored maps is active at the moment. Especially, the problem with decoding emerges with a need to analyze data at high temporal resolution. Here, we introduce a functional-connectivity-based decoder, which accounts for the pairwise correlations between the spiking activities of neurons in each map and does not require any positional information, i.e. any knowledge about place fields. We first show, on recordings of hippocampal activity in constant environmental conditions, that our decoder outperforms existing decoding methods in CA1. Our decoder is then applied to data from teleportation experiments, in which an instantaneous switch between the environment identity triggers a recall of the corresponding spatial representation . We test the sensitivity of our approach on the transition dynamics between the respective memory states (maps). We find that the rate of spontaneous state shifts (flickering) after a teleportation event is increased not only within the first few seconds as already reported, but this instability is sustained across much longer (> 1 min.) periods.
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Acknowledgements
We are indebted to S. Rosay, who contributed to the early stage of the data analysis. We are grateful to J. Tubiana and A. Treves for useful discussions and suggestions. This study benefited from partial fundings from the CNRS-InphyNiTi INFERNEUR project and from GACR 15-20008S, Progres Q-39 and NPU I LO1503 of the Czech Republic.
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Appendices
Appendix A: ACE inference convergence details
The ACE inference procedure of Ising model parameters was applied with L 2-norm regularization of strength γ = 5/B, where B is the total number of time bins (Barton et al. 2016). Details on the convergence are given in Table 2. The full code for Adaptive Cluster Expansion can be downloaded from the GitHub repo https://github.com/johnbarton/ACE/.
Appendix B: Comparison of neuron activities across spatial maps
Similarly to Fig. 2 where we compare the Ising parameters inferred from the population activity in the two environments A,B, we show in Fig. 9 the probabilities of firing of all cells i (in fixed time bins with Δt = 120 ms) and the pariwise correlations (defined as the probability that cells i,j fire together in a bin minus the product of their individual firing probabiltiies). We see that no substantial correlation is found in the pairwise statistics of cells across the two environments.
Comparison between correlations and averages of the two maps of the cross-validation reference sessions. Fixed binning with Δt = 120 ms
Appendix C: Dependence of J i j on temporal binning
Couplings inferred for time-bin duration Δt = 120 ms are compared to the ones inferred for Δt = 10 ms in Fig. 10. Many couplings are very similar across the two binning choices. Differences, in particular null couplings in just one of the two cases, mostly arise from sampling differences. For 10 ms time windows, it is rare to find two neurons active within the same time bin, while, for larger time bins, there is a smaller number B of time bins, which forces us to consider larger ACE threshold 𝜃. Couplings inferred using the theta-binning discretization procedure for data are very similar to the ones inferred using a fixed time binning of 120 ms (average duration of theta cycles), see Fig. 11. A discussion of the independence of Ising couplings from the bin duration Δt was done by Cocco et al. (2009).
Scatter plot of couplings inferred with time bin Δt = 120 ms vs. Δt = 10 ms (fixed time bin discretization procedure, from cross-reference data set)
Scatter plot of couplings inferred with fixed time bin vs. theta-binning procedure (Δt = 120 ms, from cross-reference data set)
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Posani, L., Cocco, S., Ježek, K. et al. Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings. J Comput Neurosci 43, 17–33 (2017). https://doi.org/10.1007/s10827-017-0645-9
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DOI: https://doi.org/10.1007/s10827-017-0645-9