2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-2A.19
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Representation of uncertainty during hippocampal theta sequences
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1143-0
Authors: Balázs Ujfalussy, Márton Kis, MTA Institute of Experimental Medicine, Hungary; Gergő Orbán, MTA Wigner Research Center for Physics, Hungary
Abstract: Animals are able to perform probabilistic computations implying that the nervous system is capable of the representation and manipulation of probability distributions. However, the way encoded distributions are related to population activity of neurons remains unknown because measures that could dissociate alternative models based on experimental data are remarkably lacking. Here, we focus on hippocampal activity during exploratory behavior, where the place cell activtions outline the trajectory of the animal starting from past towards future positions during each theta cycle (theta sequences). Critically, during a single theta sequence the uncertainty is expected to change systematically, thus providing an opportunity to identify how it is encoded in the population activity. We derived contrasting predictions for four alternative models: (a) encoding the most likely trajectory; (b) sampling from the posterior distribution; (c) standard probabilistic population coding and (d) distributed distributional code. We have started to apply these results to experimental data to identify if and how uncertainty of spatial trajectories are represented in the hippocampus. Our analysis framework is an important step towards elucidating the strategies used by the brain to encode probability distributions and to understand the computational role of neuronal variability.