LAUSR

Sensors are an essential element in a wide range of applications. As the number of sensors increases, so does the amount of data collected with them. This raises the challenge of efficiently processing this data. Spiking Neural Networks (SNNs) represents a promising approach to solve this problem through event-based, parallelized data processing. For SNNs to be genuinely efficient, some fundamental challenges arise, like converting analog signals to spike events. An emerging possibility is the use of Resonate-and-Fire (R&F) neurons, capable of reacting to specific frequency components of input signals. In this work, we present a possible analog implementation for a R&F neuron and show the practical encoding of analog signals into a spiking domain using actual measurements. The coding method allows analog sensor signals to be directly applied to SNNs for efficient data processing. In the future, this approach can potentially enable the direct integration of analog Spiking Neural Networks into sensors.