Titlebook: Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems; Applications in Dron Ali Safa,Lars Keuninckx,Francky Catthoor Book

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Introduction, background theory on Spiking Neural Networks (SNNs) is introduced. Then, the chapter reviews a number of emerging application domains and technological paradigms that will be covered in the next chapters of this book, namely the use of SNNs and neuromorphic technologies for drone navigation and the

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最初

A Top-Down Approach to SNN-STDP Networks,) learning, as empirically observed in the visual cortex (as opposed to the bottom-up SNN-STDP setup presented in most prior works). In contrast to empirical parameter search used in most previous works, this chapter also provides novel theoretical grounds for SNN and STDP parameter tuning which con

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Continually Learning People Detection from DVS Data,TDP) can learn to detect people on the fly from non-independent and identically distributed (non-i.i.d.) streams of retina-inspired, event camera data. The system presented in this chapter works as follows. First, a short sequence of event data, capturing a walking human from a flying drone, is forw

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Active Inference in Hebbian Learning Networks, dynamical agents. A generative model capturing the environment dynamics is learned by a network composed of two distinct Hebbian ensembles: a posterior network, which infers latent states given the observations, and a state-transition network, which predicts the next expected latent state given cur

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