eISSN 2093-744X
pISSN 1598-2645
pISSN 1598-2645
ANN-SNN conversion algorithms
| Algorithm | Neuron model | Architecture | Input encoding | Output decoding | Features |
|---|---|---|---|---|---|
| soft-LIF (2015, [44]) | soft-LIF (ANN) | Deep network | Spike train (rate code) | Spike train (firing rate) | Use soft-LIF in ANN for LIF |
| Cao et al. (2015, [45]) | ReLU (ANN) | Shallow network | Spike train (rate code) | Spike train (firing rate) | Constrained arch.; avg. pooling, no bias |
| Diehl et al. (2015, [46]) | ReLU (ANN) | Shallow network | Spike train (rate code) | Spike train (firing rate) | Constrained arch.; weight normalization |
| Rueckauer et al. (2017, [30]) | ReLU (ANN) | Deep network | Direct input | Spike train (firing rate) | Constrained arch.; batch norm.; softmax |
| Whetstone (2018, [47]) | bReLU (ANN) | Deep network | Spike train (rate code) | Spike train (firing rate) | Adaptive sharpening of activation function |
| Sengupta et al. (2019, [48]) | ReLU (ANN) | Deep network | Spike train (rate code) | Spike train (firing rate) | Normalization in SNN; Spike-Norm |
| RMP-SNN (2020, [49]) | ReLU (ANN) | Deep network | Spike train (rate code) | Spike train (firing rate) | IF with soft-reset; control threshold range; threshold balancing |
| Deng et al. (2021, [50]) | thr. ReLU (ANN) | Deep network | Spike train (rate code) | Spike train (firing rate) | Conversion loss-aware bias adaptation; threshold ReLU; shifted bias |
| Ding et al. (2021, [51]) | RNL (ANN) | Deep network | Spike train (rate code) | Spike train (rate code) | Optimal scaling factors for threshold balancing |
| Patel et al. (2021, [52]) | mod. ReLU (ANN) | Scaled-down | Spike train (rate code) | Spike train (rate code) | image segmentation Loihi deployment |
International Journal of Fuzzy Logic and Intelligent Systems 2021;21:317~337 https://doi.org/10.5391/IJFIS.2021.21.4.317
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