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A Simple Model of Optimal Population Coding for Sensory Systems

Figure 7

A variety of equally optimal solutions obtained under different resource constraints.

Each panel shows a subset of five pairs of neural encoding (top, ) and decoding (bottom, ) filters in the foveal setting at four sensory SNRs (columns, −10 to 20 dB) in four conditions (rows): (a) No additional constraint (i.e., the base model). (b) Weight sparsity. (c) Response sparsity. (d) Spatial locality. Only the spatial locality constraint yields center-surround receptive fields. See Figure S6 for the resource costs in respective populations. Note that in (d) the center-surround structure is seen only in the filters, which transform the observed signal into the neural code (and hence correspond to receptive fields). The decoding filters have a different, gaussian-like structure. These features are used to optimally reconstruct the original signal from the neural code.

doi: https://doi.org/10.1371/journal.pcbi.1003761.g007