Inference of effective networks from neural data to better understand spatial navigation

As an animal moves in space and receives external sensory inputs, it must dynamically maintain the representations of its position and environment at all times. How the hippocampus, a brain area crucial for spatial representations, achieves this task, and manages possible conflicts between different inputs remains unclear.

To study this question we have re-analyzed the data from the “teleportation” experiment [1] in which a moving rat is submitted to rapid changes of contextual (light) cues, triggering back-and-forth instabilities between two cognitive representations.

We have developed a dual neural activity decoder, capable of independently identifying the recalled cognitive map at high temporal resolution (comparable to theta cycle) and the position of the rodent given a map.

Remarkably, position can be reconstructed at any time with an accuracy comparable to fixed-context periods, even during highly unstable periods.

To explain this result we have introduced an attractor neural network model for the hippocampal activity that process inputs from external cues and the path integrator. Our model allows us to make predictions on the frequency of the cognitive map instability, its duration, and the detailed nature of the place-cell population activity, which are validated by a further analysis of the data.

 Reference:

Jezek K, Henriksen EJ, Treves A, Moser EI, Moser MB. Theta-paced flickering between place-cell maps in the hippocampus. Nature.

2011;478(7368):246–249.

Posani L, Cocco S, Monasson R. Integration and multiplexing of positional and contextual information by the hippocampal network PLOS Computational Biology, 2018 https://doi.org/10.1371/journal.pcbi.1006320

Posani L, Cocco S, Ježek K, Monasson R. Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings.

Journal of Computational Neuroscience. 2017;43(1) 17-33.