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Strong feedback excitation destabilizes the network's background state, repelling the network toward a stable attractor. Climbing activity in the model resembles neural activity in a number of cortical regions during experimental tasks with a timing requirement in this range [9], [69], [73], [84]–[89] and our analysis of network dynamics formally characterizes the underlying mechanism. The model's interval estimates conform to Weber's law, widely observed experimentally (Figures 6 and 5) and the CV of the these estimates is consistent with that of experimental subjects in timing tasks in the same range (Figure 5). Our derivation of the probability density of interval estimates reveals Weber's law as a mathematical property of the proposed timing mechanism (Figure 7). Consistent with the hypothesis that generic properties of local cortical circuits support spatial and temporal processing [12], an identical downstream network makes decisions in simulations of a perceptual discrimination task, producing psychometric and chronometric curves consistent with experimental data ( e.g. A simple learning rule is sufficient for the network to learn new intervals within a handful of trials (Figure 8), as observed across experimental paradigms and species [7], and we have demonstrated plausible mechanisms for starting and stopping the estimates (Figure 9). Because the dynamics evolve more quickly with stronger feedback excitation, estimates are shorter with stronger intrinsic NMDAR conductance. [9], [97], [98]) and demonstrating the SAT as a function of the learned temporal estimates upstream (Figure 11). Using a generic, local-circuit cortical model, we have proposed a neural mechanism for the encoding of time in the hundreds of milliseconds range.