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. 2020 Jun 19;16(6):e1007558. doi: 10.1371/journal.pcbi.1007558

Fig 8. Optimal HSNN enhances robustness and outperforms single-layer generalized linear model networks with matched linear and nonlinear receptive field transformation.

Fig 8

(a) Linear STRFs obtained at the output of the HSNN are used as to model the linear receptive field transformation of each neuron (see Methods). The LP network consists of an array of linear STRFs followed by a Poisson spike generator. The LNP network additionally incorporates a rectifying output stage following each STRF. (b) The optimal HSNN outperformance the LP network with an average performance improvement of 21.7% across SNRs. Nonlinear output rectification in the LNP network improves the performance to within 2% of the HSNN at 20 dB SNR. However, the average LNP performance was 7% lower than the optimal HSNN and performance degraded systematically with increasing noise levels (13.75% performance reduction at -5 dB SNR) demonstrating enhanced robustness of the optimal HSNN. (c) Performance improvement of each of the tested models compared against the performance of the high-resolution network.