Table 2 Comparison between ANN and SNN based on different criteria
Criteria | ANN | SNN | Neuromorphic twin |
|---|---|---|---|
Biomimicry | Far from biology | Close to biological principles | SNN |
Learning | Many techniques (backpropagation) | More complex learning rules (STDP) | ANN |
Applications | Vision, image processing | Robotics, BMI | SNN |
Robustness | Sensitive to perturbations | Robust to noise and perturbations | SNN |
Implementation | Advanced tools (PyTorch, Tensorflow) | Difficult and time-consuming | ANN |
Energy | High power consumption | Low power consumption | SNN |
Real-time | Challenging | Efficient | SNN |
Platform | Conventional CPU or GPU | Neuromorphic platforms (Loihi, etc.) or custom hardware | SNN |
