Figure 1. Overview of the Five-Layer Feedforward Spiking Neural Network.

As in HMAX [7], we alternate simple cells that gain selectivity through a sum operation, and complex cells that gain shift and scale invariance through a max operation (which simply consists of propagating the first received spike). Cells are organized in retinotopic maps until the S2 layer (inclusive). S1 cells detect edges. C1 maps subsample S1 maps by taking the maximum response over a square neighborhood. S2 cells are selective to intermediate-complexity visual features, defined as a combination of oriented edges (here we symbolically represented an eye detector and a mouth detector). There is one S1–C1–S2 pathway for each processing scale (not represented). Then C2 cells take the maximum response of S2 cells over all positions and scales and are thus shift- and scale-invariant. Finally, a classification is done based on the C2 cells' responses (here we symbolically represented a face/nonface classifier). In the brain, equivalents of S1 cells may be in V1, S2 cells in V1–V2, S2 cells in V4–PIT, C2 cells in AIT, and the final classifier in PFC. This paper focuses on the learning of C1 to S2 synaptic connections through STDP.