Neural Sequences as an Optimal Dynamical Regime for the Readout of Time. Shanglin Zhou et al

Published: 23-12-2020| Version 1 | DOI: 10.17632/mz357z8pwx.1
Shanglin Zhou,
Sotiris Masmanidis,
Dean Buonomano


We combined large-scale recordings and modeling to compare population dynamics between premotor cortex (M2) and striatum (dorsal lateral striatum) in mice performing a two-interval timing task. Conventional decoders revealed that the dynamics within each area encoded time equally well, however, the dynamics in striatum exhibited a higher degree of sequentiality. Analysis of premotor and striatal dynamics, together with a large set of simulated prototypical dynamic regimes, revealed that regimes with higher sequentiality allowed a biologically-constrained artificial downstream network to better read out time. These results suggest that although different strategies exist for encoding time in the brain, neural sequences represent an optimal and flexible dynamical regime for enabling downstream areas to read out this information. Here we provided the behavior and electrophsiological recording data of 8 mice used in the paper. We also provided matlab scripts to replicate Figure 2EFG, one of the main results in this paper as follows: --Start by running 'Fig2EFG.m' --Behavior and Ephys data of 8 mice used in the paper were stored in file 'twoIntervalData.mat', which was called by 'Fig2EFG.m'. --'SeqIndexDB': function used to compute the sequentiality index, temporal sparsity and peak entropy. --'CONV_RASTER': function used to estimate firing rate by convolving a spike train with a exponential filter.


Steps to reproduce

Please see the Star methods of the paper for details.