Spiking Machine Intelligence: What we can learn from biology and how spiking Neural Networks can help to improve Machine Learning

Richard C. Gerum, Achim Schilling

Up to now, modern Machine Learning is based on fitting high dimensional functions to enormous data sets, taking advantage of huge hardware resources. We show that biologically inspired neuron models such as the Integrate-and-Fire (LIF) neurons provide novel and efficient ways of information encoding. They can be integrated in Machine Learning models, and are a potential target to improve Machine Learning performance. Thus, we systematically analyze the LIF neuron. We start by deriving simple integration equations to which even a gradient can be assigned. Additionally, we prove that a Long-Short-Term-Memory unit can be tuned to show similar spiking properties. Additionally, LIF units are applied to an image classification task, trained with backpropagation. With this study we want to contribute to the current efforts to enhance Machine Intelligence by integrating principles from biology.

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