## 3 Physical and Mixed Safety
### Trustworthy and Explainable SNNs
Hence, our main concerns about implementing the solutions in such applications are the low availability of neuromorphic processors, which are necessary for the low energy demand of such systems, and the need for scaling up the proposed solutions, which are quite promising. We found few papers focusing on the explainable SNN field, which signalizes that it is still a poorly developed area in the SNNs domain. [PERSON], in her Master's thesis [97], proposed the Temporal Spike Attribution (TSA) method, which allows for a better understanding of the SNN's outcome. Next, [PERSON] and [PERSON] [98] basically proposed a tool that produces a heatmap of the input shape, showing an attention map for the input data, named Spike Activation Map (SAM). The authors claimed that this is the beginning (in 2021) of the \"explainable neuromorphic computing\" area that is leading to increased trust in SNNs' outcomes. Finally, [PERSON] et al. (2022) [99] focused on detecting abnormal input data by applying the Out-of-Distribution (OoD) data detector. The OoD input is the input data that the model was never trained with. Hence, it can produce untrustworthy outcomes. Their detector helps to expose the OoD input by observing hidden layers of the SNN. Moreover, the detector is able to indicate which part of the input is the most-atypical, yielding the map of the input abnormality. The authors compared their work to other OoD detection schemes, claiming that their method is competitive with them. Further, we explored DVS safety and robustness solutions. A DVS is a device that detects scene illumination changes. A DVS is also called a silicon retina or event camera. Each pixel of a device's matrix reacts to changes in its field of view. The sensor produces a stream of asynchronous events: it elicits an event at the moment of brightness change detection. A particular event encodes a few pieces of information, i.e., time, pixel location, and the sign of the brightness change. In contrast to traditional cameras, DVSs have some attractive properties--instantaneous reaction for the changes in the scene (temporal resolution in the order of \(\upmu\)s instead of the order of many ms in traditional cameras), high dynamic ranges (up to 140 dB instead of \(\sim\)60 dB), and low energy consumption. More details on DVSs were given in the survey by [PERSON] et al. [100]. Event sensors are a kind of native source of information for SNNs, due to their event asynchronous nature, similar to neuronal spikes. [PERSON] et al. [101] explored three aspects of processing events from the DVS cameras with SNNs. First was a conversion of a large-scale CNN to the SNN without losing too much precision. The solutions they proposed for conversion between network types can be generalized to convert other DNNs into SNNs. Secondly, they transformed the Cityscapes dataset [102] into two different representations: event-processing mode and contrast-detection mode. Lastly, they constructed a 3D-structured-light-acquisition system and 3D-image-recognition algorithm, using a DVS camera from Prophesee, Model PSEE300 EVK. The authors stated that their algorithm achieved good generalization results. [PERSON] et al. [103] proposed the Temporalwise Attention SNN (TA-SNN) to classify event streams coming from DVS cameras with higher accuracy. The authors observed that event streams include many redundant events in the temporal dimension. Hence, their TA module estimates how much attention the SNN should pay to the current, temporarily created time frame. The time frame was constructed from events belonging to a particular time window. In the inference mode, the attention value must surpass a certain threshold for further event processing (utilizing the SNN). Otherwise, the processing is skipped. In the training mode, the attention mechanism weights inflowing time frames.