The models in Fig 8 were in qualitative agreement with experimental behavioral and neural data, but the quantitative match of neuronal firing rates was limited by the network implementation. To evaluate quantitative predictions that took into account the specific firing rates and heterogeneity present in the data (Fig. 1), we tested two coding models for inaccurate memory-guided behavioral responses: the bump attractor model and the decaying bump model. (a) In the bump attractor model, dispersion of behavioral responses is due to noise-induced diffusion of the location of a rigid bump representation. In different trials (red and blue) in response to the same cue the bump diffuses during the delay to different locations (lower panel), giving rise to different read-outs and inaccurate, off-target behavioral responses. (b) In the decaying bump model, the bump is established at cue presentation, but begins to decay once the cue is removed. During the delay, the coding slowly decays away, with different decay rates in different trials. The read-out is inaccurate at the end of the delay due to the decreased selectivity. (c-d) Generation of multiple firing rate trials (8 equidistant cues, 10 trials per cue) and surrogate Poisson spike trains for n = 200 neurons from the models in corresponding panels a and b above generates behavioral and neural data in quantitative agreement with the experimental data (Fig. 1). Behavioral responses from computational models (panel c, left, and panel d, right) are represented as in Fig. 1b. The memory dependence of these behavioral inaccuracies is revealed by comparing with the models' behavioral responses for a brief 0.5 s delay (black dashed curve). Average tuning curves of model neurons (panel c, right and panel d, left) in the delay period computed as in Fig. 1d.