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Spike-Based Population Coding and Working Memory

Figure 3

Network performance.

(A) Input and output spike trains on a single trial. A stimulus with constant drift and diffusion is presented for 500 ms (gray area). (B) Time evolution of the stimulus posterior for the ideal observer (blue) and the network read-out (red). Thick lines show the mean of the posterior and narrow lines the corresponding width. The stimulus trajectory is shown in black. The dashed black line indicates the predictable (drift) part of the stimulus that the network is tracking during the memory period. (C) Snapshots of the posteriors, from left to right; after 500ms (end of integration period), after 2000 ms and after 5000 ms. (D) Coding performance measured as the standard deviation of the stimulus estimate around its real value . The blue and red curves depict the performance of the ideal observer and the network respectively and the green curve shows the performance of a network without slow currents . (E) Width of the posterior decoded from the ideal observer (blue), the full network model (described in equations 7 and 8) (red), a network in which we approximate the nonlocal term in the slow currents by a linear term (see equation 10) (green) and a network for which we completely remove the nonlocal term (magenta).

Figure 3

doi: https://doi.org/10.1371/journal.pcbi.1001080.g003