A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks

Hinkel, Georg; Groenda, Henning; Krach, Sebastian; Vannucci, Lorenzo; Denninger, Oliver; Cauli, Nino; Ulbrich, Stefan; Roennau, Arne; Falotico, Egidio; Gewaltig, Marc‐Oliver; Knoll, Alois; Dillmann, Rüdiger; Laschi, Cecilia; Reussner, Ralf

doi:10.1007/s10846-016-0412-6

Cited by 18 publications

(13 citation statements)

References 23 publications

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“…This platform allows the user to easily transfer data between the two simulations by implementing transfer functions that convert data coming from one simulation into suitable inputs for the other (Hinkel et al, 2017). In our case, we had to integrate the NEST spindle model in the list of possible devices and then develop the transfer functions for the specific setups.…”

Section: Resultsmentioning

confidence: 99%

“…First, the model was tested by embedding the NEST implementation in the Neurorobotics Platform, a simulation tool that is able to coordinate physical and neural simulations to create neurorobotic action-perception closed loops (Falotico et al, 2017 ). This platform allows the user to easily transfer data between the two simulations by implementing transfer functions that convert data coming from one simulation into suitable inputs for the other (Hinkel et al, 2017 ). In our case, we had to integrate the NEST spindle model in the list of possible devices and then develop the transfer functions for the specific setups.…”

Section: Resultsmentioning

confidence: 99%

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Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model

2017

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Connecting biologically inspired neural simulations to physical or simulated embodiments can be useful both in robotics, for the development of a new kind of bio-inspired controllers, and in neuroscience, to test detailed brain models in complete action-perception loops. The aim of this work is to develop a fully spike-based, biologically inspired mechanism for the translation of proprioceptive feedback. The translation is achieved by implementing a computational model of neural activity of type Ia and type II afferent fibers of muscle spindles, the primary source of proprioceptive information, which, in mammals is regulated through fusimotor activation and provides necessary adjustments during voluntary muscle contractions. As such, both static and dynamic γ-motoneurons activities are taken into account in the proposed model. Information from the actual proprioceptive sensors (i.e., motor encoders) is then used to simulate the spindle contraction and relaxation, and therefore drive the neural activity. To assess the feasibility of this approach, the model is implemented on the NEST spiking neural network simulator and on the SpiNNaker neuromorphic hardware platform and tested on simulated and physical robotic platforms. The results demonstrate that the model can be used in both simulated and real-time robotic applications to translate encoder values into a biologically plausible neural activity. Thus, this model provides a completely spike-based building block, suitable for neuromorphic platforms, that will enable the development of sensory-motor closed loops which could include neural simulations of areas of the central nervous system or of low-level reflexes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model

2017

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“…All the simulations were run on the Neurorobotics Platform and implemented through its utilities, which has been shown capable of implementing robotic control loops (Vannucci et al, 2015). The controller was implemented using a domain-specific language that eases the development of robotic controllers, and that is part of the Neurorobotics Platform simulation engine (Hinkel et al, 2017). Another tool, called Virtual Coach and also included in the platform and employed to implement the evolutionary algorithm.…”

Section: Methodsmentioning

confidence: 99%

Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion

et al. 2019

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In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs. This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment. Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space.

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“…A relatively simple option to integrate a Draculab controller in a virtual environment is provided by the HBP neurorobotics platform (NRP) (Hinkel et al, 2017) (https://neurorobotics.net). The NRP calls a Python transfer function on each simulation step, and Draculab can be used in it.…”

Section: Beyond the Core Simulatormentioning

confidence: 99%

Draculab: A Python Simulator for Firing Rate Neural Networks With Delayed Adaptive Connections

Verduzco-Flores

Schutter

2019

Front. Neuroinform.

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Draculab is a neural simulator with a particular use scenario: firing rate units with delayed connections, using custom-made unit and synapse models, possibly controlling simulated physical systems. Draculab also has a particular design philosophy. It aims to blur the line between users and developers. Three factors help to achieve this: a simple design using Python's data structures, extensive use of standard libraries, and profusely commented source code. This paper is an introduction to Draculab's architecture and philosophy. After presenting some example networks it explains basic algorithms and data structures that constitute the essence of this approach. The relation with other simulators is discussed, as well as the reasons why connection delays and interaction with simulated physical systems are emphasized.

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A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks

Cited by 18 publications

References 23 publications

Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model

Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model

Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion

Draculab: A Python Simulator for Firing Rate Neural Networks With Delayed Adaptive Connections

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