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eess.SP2024
On the Sampling Sparsity of Neuromorphic Analog-to-Spike Conversion based on Leaky Integrate-and-Fire
Bernhard A. Moser, Michael Lunglmayr
In contrast to the traditional principle of periodic sensing neuromorphic engineering pursues a paradigm shift towards bio-inspired event-based sensing, where events are primarily…
cs.NE2024
On the Solvability of the {XOR} Problem by Spiking Neural Networks
Bernhard A. Moser, Michael Lunglmayr
The linearly inseparable XOR problem and the related problem of representing binary logical gates is revisited from the point of view of temporal encoding and its solvability by sp…
cs.NE2024
On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals
Bernhard A. Moser, Michael Lunglmayr
Leaky-integrate-and-fire (LIF) is studied as a non-linear operator that maps an integrable signal to a sequence of discrete events, the spikes. In the case without any Di…