Information theoretic measures of neural and behavioural coupling predict representational drift
Fig 2
Redundancy index correlates with tuning stability.
(a) Constructing a multivariate linear model for stability with three regressors: mutual information (MI), redundancy index (Red), and synergy index (Syn). Pair plots showing the stability plotted against each of the predictors for one mouse on a single session (Driscoll et al. [1], 130 neurons, day 5, right turns). (b) The three regressors are all strongly correlated with each other. Each data point represents a neuron from one mouse on a given day (Driscoll et al. [1], 130 neurons, 17 days). The redundancy index is plotted against the synergy index, with the size and colour showing the mutual information and stability, respectively. The session-averaged correlation coefficient ρ between each pair of regressors is given in the bottom right. (c) Regularised regression coefficients for the three predictors with position and heading as the target external variable [1]. Each data point represents a given session and trial type (left or right turns) for a given mouse. (n = 617 PPC neurons across 4 mice.) (d) Same as (c) but with gratings and movies as the external variable [4]. (n = 1053 V1 neurons across 4 mice.)