I’m wondering if anyone knows of any example of using spiking neural networks for habituation with sensors/sensor systems? In other words, the idea that when a sensor’s signal or signal statistics stay the same for a while, the response drops off.
I’ve read about this phenomenon in some neuroscience literature that discussed synaptic filtering, like this review paper (see under “Adaptation and enhancement of transients” but I’m just curious about if anyone actually tried something like it in an “engineering” context. Seems like there could be some interesting applications. Thanks!