Training a spiking neural network to control a 4-DoF robotic arm based on Spike Timing-Dependent Plasticity

Bouganis, Alexandros; Shanahan, Murray

doi:10.1109/ijcnn.2010.5596525

Cited by 78 publications

(43 citation statements)

References 21 publications

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“…Izhikevich [43] presented an SNN model that has been praised for both its computational efficiency and its accurate response. In robot control, Bouganis and Shanahan [44] used a network of 12,000 Izhikevich neurons to learn a local mapping from two configurations, i.e., four DOF of an iCub arm, and two target locations in 3-D Cartesian space, to two joint actions through motor babbling. Other robot learning work using SNNs include navigational control applications [45], swinging-up and balancing the acrobot [46], and the development of wall following behaviors [47].…”

Section: B Connectionist Approachesmentioning

confidence: 99%

Humanoid Tactile Gesture Production using a Hierarchical SOM-based Encoding

Pierris

Dahl

2014

IEEE Trans. Auton. Mental Dev.

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The existence of cortical hierarchies has long since been established and the advantages of hierarchical encoding of sensor-motor data for control, have long been recognized. Less well understood are the developmental processes whereby such hierarchies are constructed and subsequently used. This paper presents a new algorithm for encoding sequential sensor and actuator data in a dynamic, hierarchical neural network that can grow to accommodate the length of the observed interactions. The algorithm uses a developmental robotics methodology as it extends the Constructivist Learning Architecture, a computational theory of infant cognitive development. This paper presents experimental data demonstrating how the extended algorithm goes beyond the original theory by supporting goal oriented control. The domain studied is the encoding and reproduction of tactile gestures in humanoid robots. In particular, we present results from using a Programming by Demonstration approach to encode a stroke gesture. Our results demonstrate how the novel encoding enables a Nao humanoid robot with a touch sensitive fingertip to successfully encode and reproduce a stroke gesture in the presence of perturbations from internal and external forces.

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Section: B Connectionist Approachesmentioning

confidence: 99%

Humanoid Tactile Gesture Production using a Hierarchical SOM-based Encoding

Pierris

Dahl

2014

IEEE Trans. Auton. Mental Dev.

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“…For example, Bouganis et al [20] used a spiking neural network with STDP to map spatial targets and proprioceptive information into motor commands for a robotic arm. Their network was strictly feedforward and relied on firing rates to encode both inputs and outputs.…”

Section: Spiking Neural Network and Motor Controlmentioning

confidence: 99%

A spiking neural network model of canonical babbling development

Warlaumont

2012

2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL)

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The author wished to add the following correction:"After publication, it was discovered that the model did not receive reinforcement in the way that the paper described. The main conclusions still hold after correcting the error and making the following changes: performing synaptic weight normalization in place of spike-timing-dependent depression, removing the constant low level of dopamine, and scaling muscle activity by 2.5. A simulation and yoked control of the corrected model exhibited an increase in performance over time in the main simulation (r = 0.096, p < 0.001) as well as, to a lesser extent, in the control (r = 0.070, p = 0.008), and performance was significantly higher overall in the main simulation compared to control (T = 1.870, p = 0.031)."Abstract-Canonical babbling, the production of vocalizations that contain mature-sounding syllables, is one of the most striking and important milestones prior to the production of first words. This study simulates the emergence of canonical babbling using a spiking neural network containing motor neurons that activate muscles in a vocal tract simulator. The spiking neural network periodically produces synthesized vocalizations and a human listener judges the vocalizations on the basis of their syllabicity, deciding whether or not to reward the model. If a reward is given, spike timing dependent plasticity is increased and the model becomes more likely to recreate a pattern of neural firings similar to that which generated the reinforced vocalization. The model successfully increases its production of mature-sounding canonical syllables, whereas a yoked control simulation does not exhibit any such effect. This finding corresponds to results of experimental work with human infants in which consonantvowel syllable production is selectively reinforced by the infants' caregivers.

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“…Bouganis and Shanahan [27] present a spiking neural network architecture that autonomously learns to control a 4-degrees-of-freedom robotic arm after an initial period of motor babbling. Its aim is to provide the joint commands that will move the end-effector in a desired spatial direction, given the joint configuration of the arm.…”

Section: Neural Network Controllersmentioning

confidence: 99%

A Cognitive Model for Generalization during Sequential Learning

Gupta

Vig

Noelle

2011

Journal of Robotics

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Traditional artificial neural network models of learning suffer from catastrophic interference. They are commonly trained to perform only one specific task, and, when trained on a new task, they forget the original task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework, specifically fast lateral inhibition and a local synaptic plasticity model that incorporates both correlational and error-based components, are sufficient to largely overcome this limitation during the sequential learning of multiple motor skills. Evidence has also provided that Leabra is able to generalize the subsequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization exhibited by the backpropagation of error learning algorithm. Furthermore, we demonstrate the applicability of sequential learning to a pair of movement tasks using a simulated robotic arm.

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Training a spiking neural network to control a 4-DoF robotic arm based on Spike Timing-Dependent Plasticity

Cited by 78 publications

References 21 publications

Humanoid Tactile Gesture Production using a Hierarchical SOM-based Encoding

Humanoid Tactile Gesture Production using a Hierarchical SOM-based Encoding

A spiking neural network model of canonical babbling development

A Cognitive Model for Generalization during Sequential Learning

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