Fig. 6: Medial frontal activity predicts distal behavioural choices.

a, Schematic showing distal prediction of choices from memory buffers. The SMB model enables us to predict the choices made by the mouse: (1) at a precise lag in the future; and (2) in a way that generalizes across tasks. The size and the timing of the activity bump determines how likely and when (respectively) the animal will visit the SMB’s anchor in the next trial. This future prediction should generalize across tasks and thus be independent of where the mouse is at a given time. In this example we show an SMB with an anchor at location 2 (top middle location shaded in brown) at intermediate goal progress (halfway between goals). The brown neuron is the anchor, whereas the green neuron fires at a lag of 1.5 states (135°) from the anchor. Four rows correspond to four possible scenarios across two different tasks, illustrating the key features of the SMB model’s predictions. The activity of the green neuron at precisely 1.5 states since the first anchor visit (t₂; the bump time of the green neuron) can be used to predict what will happen at t₃ (2.5 states forward from t₂). A larger activity bump (indicated by the height of the bump along the SMB) increases the likelihood that the animal will return to the anchor at t₃. This larger bump is indexed by higher activity of the green neuron at t₂ (indicated by a darker green shade). In task Y, the green neuron fires at a different location in order to keep its lag from its anchor. Nevertheless, its activity at t₂ can be used in the same way to predict whether the mouse will visit the anchor at t₃ (2.5 states in the future). To test this latter point, we only consider non-zero lag cells, which fire in different locations across tasks, in all of the analyses below. Reproduced/adapted with permission from Gil Costa. b, Design of logistic regression to assess the effect of each neuron’s activity on future visits to its anchor. To control for autocorrelation in behavioural choices, previous choices as far back as ten trials in the past are added as co-regressors. Separate regressions are done for activity at different times: bump time, random times, decision time (the time where the mouse was one spatial step away, and one goal-progress bin away from the anchor of a given neuron) and times shifted by 90° intervals relative to the neuron’s bump time. c, Bottom, regression coefficients are significantly positive for the bump time but not for any of the other control times. Two-sided t-tests against 0 for bump time: n = 131 tasks, t-statistic = 2.75, P = 0.007, d.f. = 130; decision time: n = 131, t-statistic = −0.77, P = 0.446, d.f. = 130; random time: n = 131, t-statistic = −0.79, P = 0.433, d.f. = 130; 90° shifted time: n = 131, t-statistic = −2.74, P = 0.007, d.f. = 130; 180° shifted time: n = 131, t-statistic = −1.47, P = 0.143, d.f. = 130; 270° shifted time: n = 131, t-statistic = −2.54, P = 0.012, d.f. = 130. Top, swarm plots showing distribution of regression coefficient values across groups. Data are mean ± s.e.m.