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For a biological agent operating under environmental pressure, energyconsumption and reaction times are of critical importance. Similarly,engineered systems are optimized for short time-to-solution and lowenergy-to-solution characteristics. At the level of neuronal implementation,this implies achieving the desired results with as few and as early spikes aspossible. With time-to-first-spike coding both of these goals are inherentlyemerging features of learning. Here, we describe a rigorous derivation of alearning rule for such first-spike times in networks of leakyintegrate-and-fire neurons, relying solely on input and output spike times, andshow how this mechanism can implement error backpropagation in hierarchicalspiking networks. Furthermore, we emulate our framework on the BrainScaleS-2neuromorphic system and demonstrate its capability of harnessing the system'sspeed and energy characteristics. Finally, we examine how our approachgeneralizes to other neuromorphic platforms by studying how its performance isaffected by typical distortive effects induced by neuromorphic substrates.

Fast and energy-efficient neuromorphic deep learning with first-spike times