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Conventional sampling focuses on encoding and decoding bandlimited signals byrecording signal amplitudes at known time points. Alternately, sampling can beapproached using biologically-inspired schemes. Among these areintegrate-and-fire time encoding machines (IF-TEMs). They behave likesimplified versions of spiking neurons and encode their input using spike timesrather than amplitudes.  Moreover, when multiple of these neurons jointly process a set of mixedsignals, they form one layer in a feedforward spiking neural network. In thispaper, we investigate the encoding and decoding potential of such a layer.  We propose a setup to sample a set of bandlimited signals, by mixing them andsampling the result using different IF-TEMs. We provide conditions for perfectrecovery of the set of signals from the samples in the noiseless case, andsuggest an algorithm to perform the reconstruction.

Encoding and Decoding Mixed Bandlimited Signals using Spiking Integrate-and-Fire Neurons