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Electronic nose inhibition in a spiking neural network for noise cancellation

An olfaction detection spiking neural network that detects binary odor patterns is analyzed and implemented. This paper presents a new method for inhibiting spiking neural networks by modulating a detection threshold. Interference noise from active odors is measured by a single inhibitory neuron. The inhibition neuron changes the detection threshold to create tolerance for a system with multiple odors present. A digital implementation of the inhibition is simulated. Comparative results prove that threshold modulation reduces false-positive detection error in high noise scenarios where fifteen odors are active simultaneously.