Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
On the computational power of circuits of spiking neurons
Journal of Computer and System Sciences
CiE '07 Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World
Neuro-inspired Speech Recognition with Recurrent Spiking Neurons
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Learning anticipation via spiking networks: application to navigation control
IEEE Transactions on Neural Networks
Optimizing Generic Neural Microcircuits through Reward Modulated STDP
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Robospike Sensory Processing for a Mobile Robot Using Spiking Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Spiking neural networks for cortical neuronal spike train decoding
Neural Computation
Spiking neural networks for cortical neuronal spike train decoding
Neural Computation
What makes a brain smart? reservoir computing as an approach for general intelligence
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Modeling working memory and decision making using generic neural microcircuits
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Liquid computing in a simplified model of cortical layer IV: learning to balance a ball
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Dynamical movement primitives: Learning attractor models for motor behaviors
Neural Computation
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How can complex movements that take hundreds of milliseconds be generated by stereotypical neural microcircuits consisting of spiking neurons with a much faster dynamics? We show that linear readouts from generic neural microcircuit models can be trained to generate basic arm movements. Such movement generation is independent of the arm model used and the type of feedback that the circuit receives. We demonstrate this by considering two different models of a two-jointed arm, a standard model from robotics and a standard model from biology, that each generates different kinds of feedback. Feedback that arrives with biologically realistic delays of 50 to 280 ms turns out to give rise to the best performance. If a feedback with such desirable delay is not available, the neural microcircuit model also achieves good performance if it uses internally generated estimates of such feedback. Existing methods for movement generation in robotics that take the particular dynamics of sensors and actuators into account (embodiment of motor systems) are taken one step further with this approach, which provides methods for also using the embodiment of motion generation circuitry, that is, the inherent dynamics and spatial structure of neural circuits, for the generation of movement.