Computation by Time


Over the last years, the amount of research performed in the field of spiking neural networks has been growing steadily. Spiking neurons are modeled to approximate the complex dynamic behavior of biological neurons. They communicate via discrete impulses called spikes with the actual information being encoded in the timing of these spikes. As already pointed out by Maass in his paper on the third generation of neural network models, this renders time a central factor for neural computation. In this paper, we investigate at different levels of granularity how absolute time and relative timing enable new ways of biologically inspired neural information processing. At the lowest level of single spiking neurons, we give an overview of coding schemes and learning techniques which rely on precisely timed neural spikes. A high-level perspective is provided in the second part of the paper which focuses on the role of time at the network level. The third aspect of time considered in this work is related to the interfacing of neural networks with real-time systems. In this context, we discuss how the concepts of computation by time can be implemented in computer simulations and on specialized neuromorphic hardware. The contributions of this paper are twofold: first, we show how the exact modeling of time in spiking neural networks serves as an important basis for powerful computation based on neurobiological principles. Second, by presenting a range of diverse learning techniques, we prove the biologically plausible applicability of spiking neural networks to real world problems like pattern recognition and path planning.

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The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no 604102 (HBP).

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Correspondence to Florian Walter.

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Walter, F., Röhrbein, F. & Knoll, A. Computation by Time. Neural Process Lett 44, 103–124 (2016).

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  • Spiking neural network
  • Neurobiological learning
  • Reservoir computing
  • Hierarchical learning
  • Neural coding
  • Neuromorphic hardware