  Embedded systems acquire information about the real world from sensors and
process it to make decisions and/or for transmission. In some situations, the
relationship between the data and the decision is complex and/or the amount of
data to transmit is large (e.g. in biologgers). Artificial Neural Networks
(ANNs) can efficiently detect patterns in the input data which makes them
suitable for decision making or compression of information for data
transmission. However, ANNs require a substantial amount of energy which
reduces the lifetime of battery-powered devices. Therefore, the use of Spiking
Neural Networks can improve such systems by providing a way to efficiently
process sensory data without being too energy-consuming. In this work, we
introduce a low-powered neuron model called Integrate-and-Fire which exploits
the charge and discharge properties of the capacitor. Using parallel and series
RC circuits, we developed a trainable neuron model that can be expressed in a
recurrent form. Finally, we trained its simulation with an artificially
generated dataset of dog postures and implemented it as hardware that showed
promising energetic properties. This paper is the full text of the research,
presented at the 20th International Conference on Artificial Intelligence and
Soft Computing Web System (ICAISC 2021)
