, 2012; MacDonald et al., 2011). We then refit the data using six nested models (Figure S4A). Each nested model removed one or more categories of covariates (time, distance, or space) from the full
model. The first three nested models (space and time [“S+T”], time and distance [“T+D”], and space and distance [“S+D”]; middle row, Figure S4A) see more removed only one category of covariates (distance, space, and time, respectively). The remaining three nested models (time [“T”], space [“S”], and distance [“D”]; bottom row, Figure S4A) removed two categories of covariates. The deviance of each nested model compared to the full model quantified the effect of removing that category of covariates on the quality of the model fit. Covariates related to treadmill speed and spike history were included in all nested models. We first tested the space (“S”) nested model, which included covariates from space (as well as speed and spike history), but excluded time and distance covariates. The deviance of the “S” model from the full model quantified the effect of removing both time and distance covariates
from the full model, while accounting for any influence due to spatial movement. Thus, comparing the “S” model to the full “S+T+D” model measured the combined importance of distance and time in the model. The results from this model indicated Quizartinib that for 380/400 neurons, combined information about time and distance on the treadmill significantly improved the model fit (95%; χ210 > 18.3; p ≤ 0.05) (Figure S4B). A similar comparison of the time and distance (“T+D”) nested model to the full model indicated that 371/400 neurons showed spatial modulation (93%; χ25 > 11.1; p ≤ 0.05) in addition to the modulation due to time and distance (Figure S4C). These results are consistent with the results above (Figure 5), and show that although many neurons did demonstrate spatial tuning as a result of minor
residual variations in location, the majority of neurons demonstrated time and distance tuning PDK4 in addition to spatial tuning. Like the tuning curve method used earlier to show that hippocampal activity during treadmill running cannot be explained by spatial position (Figure 6), the “S” GLM used only spatial covariates to account for the firing properties of each neuron. The difference score from the earlier turning curve method measured how different the model prediction (using only space) was from the actual firing, and larger values indicated a larger role of time and distance in driving firing. Similarly, the deviance of the “S” GLM (using only space) from the full model (including time and distance) measured the importance of time and distance in the quality of the model fit (Figure S4B). These two distinct approaches model the firing of neurons using very different assumptions.