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Biomorphic robot controls: event driven model free deep SNNs for complex visuomotor tasks

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Abstract

The human brain surpass conventional computer architectures in regard to energy efficiency, robustness, connectivity and adaptivity. These aspects are inspiring today’s emerging biomorphic technologies into both directions, software tools, and hardware systems. Thus, it is worthwhile to investigate into biological processes which enable the brain to perform computations and how they can be modelled and implemented in silicon. Taking inspiration from how the brain performs information processing requires a shift of computational paradigms compared to conventional computer architectures. Indeed, the brain is composed of nervous cells, called neurons, connected with synapses and forming self-organized networks. Neurons and synapses are complex dynamical systems ruled by biochemical and electrical reactions. As a result, they rely on local information forming functional neural clusters. Additionally, neurons communicate with each other with short electrical pulses, called spikes, which travel across synapses. Computational neuroscientists and roboticists attempt to model these computations with spiking neural networks (SNNs) and to ground them with real robots. When implemented on dedicated neuromorphic hardware, spiking neural networks can perform time delay free, energy efficient computations in analogy to the brain. Until recently, the advantages of this technology were limited due to the lack of efficient methods for programming spiking neural networks. Reinforcement learning is one paradigm for programming spiking neural networks, in which neural clusters self-organize them towards functional networks. In this paper a survey on our research at FZI on design, implementation and experiments with SNNs, solving visuomotor tasks for biomorphic robots is given.

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Acknowledgements

This research has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 Human Brain Project SGA2 and the Baden-Wuerttemberg Science Foundation

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  1. Research Center Informatics FZI, Interactive Diagnosis Systems IDS, Karlsruhe, Germany

    Rüdiger Dillmann & Arne Rönnau

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Correspondence to Rüdiger Dillmann.

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This work was presented in part as a plenary speech at the joint symposium of the 27th International Symposium on Artificial Life and Robotics, the 7th International Symposium on BioComplexity, and the 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Online, January 25–27, 2022).

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Dillmann, R., Rönnau, A. Biomorphic robot controls: event driven model free deep SNNs for complex visuomotor tasks. Artif Life Robotics 27, 429–440 (2022). https://doi.org/10.1007/s10015-022-00769-4

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