A spiking neural network alternative for the analog to digital converter

A majority of artificial spiking neural network models are used for modeling and/or understanding biological processes, while only in a few instances have they been used for solving engineering tasks. When spiking neural networks are used for an engineering task often the inputs are fed directly into the network and it is up to the network to evolve the connections from a randomly connected pool of neurons. This approach may generate a network capable of producing the desired output (mapping), however, the resulting network is typically undecipherable. In modern digital systems a microprocessor is used to perform a task. Specifically, an analog to digital converter is used to allow the microprocessor to accurately interpret analog signals coming from sensors monitoring the task. In this paper, a spiking neural network is used to measure the intensity of an input analog signal using a coincidence detection approach, where a pool of intensity detecting neurons is responsible for identifying specific intensities. At any instant in time the active neuron in the pool represents current input intensity of the signal. We first show the circuit for a single intensity case. Next, the entire intensity detection circuit is examined over a large range of intensities. Finally, the entire network is tested when the input signal is mixed with Gaussian noise. The resulting analog coincidence intensity detection network has a rapid and accurate response while being noise-resistant. The modularity of the design allows the network to be easily incorporated as a preprocessing system in other and/or larger simulations.