Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS

Weidel, Philipp; Djurfeldt, Mikael; Duarte, Renato; Morrison, Abigail

doi:10.3389/fninf.2016.00031

Cited by 23 publications

(28 citation statements)

References 23 publications

(31 reference statements)

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“…The robotic simulation was carried out using the robotic simulator Gazebo (http://www.gazebosim.org) and the Robotic Operating System (ROS) (http://www.ros.org/) on the Pioneer3AT (http://www.mobilerobots.com/ResearchRobots/P3AT.aspx) robotic platform. To interface the neural network simulation in NEST with the robotic simulation in Gazebo, we used the ROS‐MUSIC Toolchain (Djurfeldt et al ., ; Weidel et al ., ).…”

Section: Methodsmentioning

confidence: 97%

Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework

Bahuguna

Weidel

Morrison

2018

Eur J of Neuroscience

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The basal ganglia have been hypothesized to be involved in action selection, i.e. resolving competition between simultaneously activated motor programs. It has been shown that the direct pathway facilitates action execution whereas the indirect pathway inhibits it. However, as the pathways are both active during an action, it remains unclear whether their role is co-operative or competitive. In order to investigate this issue, we developed a striatal model consisting of D1 and D2 medium spiny neurons (MSNs) and interfaced it to a simulated robot moving in an environment. We demonstrate that this model is able to reproduce key behavioral features of several experiments involving optogenetic manipulation of the striatum, such as freezing and ambulation. We then investigate the interaction of D1- and D2-MSNs. We find that their fundamental relationship is co-operative within a channel and competitive between channels; this turns out to be crucial for action selection. However, individual pairs of D1- and D2-MSNs may exhibit predominantly competition or co-operation depending on their distance, and D1- and D2-MSNs population activity can alternate between co-operation and competition modes during a stimulation. Additionally, our results show that D2-D2 connectivity between channels is necessary for effective resolution of competition; in its absence, a conflict of two motor programs typically results in neither being selected.

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Section: Methodsmentioning

confidence: 97%

Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework

Bahuguna

Weidel

Morrison

2018

Eur J of Neuroscience

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“…All neural network simulations were performed using the Neural Simulation Tool 2.16 (NEST) (Gewaltig & Diesmann, 2007; Linssen et al, 2018). The interface between NEST and the OpenAI Gym (Brockman et al, 2016) was implemented using the ROS-MUSIC Toolchain (Weidel et al, 2016; Jordan et al, 2017). Simulation scripts and model definitions are publically available 3 .…”

Section: Methodsmentioning

confidence: 99%

Unsupervised learning and clustered connectivity enhance reinforcement learning in spiking neural networks

Weidel

Duarte

Morrison

2020

Preprint

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2Reinforcement learning is a learning paradigm that can account for how organisms learn to 3 adapt their behavior in complex environments with sparse rewards. However, implementations in 4 spiking neuronal networks typically rely on input architectures involving place cells or receptive 5 fields. This is problematic, as such approaches either scale badly as the environment grows in size 6 or complexity, or presuppose knowledge on how the environment should be partitioned. Here, we 7 propose a learning architecture that combines unsupervised learning on the input projections with 8 clustered connectivity within the representation layer. This combination allows input features to 9 be mapped to clusters; thus the network self-organizes to produce task-relevant activity patterns 10 that can serve as the basis for reinforcement learning on the output projections. On the basis 11 of the MNIST and Mountain Car tasks, we show that our proposed model performs better than 12 either a comparable unclustered network or a clustered network with static input projections. We 13 conclude that the combination of unsupervised learning and clustered connectivity provides a 14 generic representational substrate suitable for further computation. 15

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“…We decided not to use MUSIC for the synchronization in this first release, even if it was shown to be working by Weidel et al (2015, 2016), in order to ease the communication between brain and world simulations without introducing any middle layer. Moreover, relying on the already existing Python APIs for the communication with the two simulators had the effect of simplify the development process.…”

Section: Software Architecturementioning

confidence: 99%

“…We embedded these simulators in a comprehensive framework that allows the user to design and run neurorobotic experiments. In line with our approach, Weidel et al (2015, 2016) proposed to couple the widely used neural simulation tool NEST (Gewaltig and Diesmann, 2007) with the robot simulator Gazebo (Koenig and Howard, 2004), using the MUSIC middleware (Djurfeldt et al, 2010). …”

Section: Introductionmentioning

confidence: 98%

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

et al. 2017

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Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.

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Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS

Cited by 23 publications

References 23 publications

Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework

Exploring the role of striatal D1 and D2 medium spiny neurons in action selection using a virtual robotic framework

Unsupervised learning and clustered connectivity enhance reinforcement learning in spiking neural networks

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

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