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Showing 1 to 12 of 712 entries
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An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking.

Frontiers in neurorobotics

Ding L, Xiao L, Liao B, Lu R, Peng H.
PMID: 28919855
Front Neurorobot. 2017 Sep 01;11:45. doi: 10.3389/fnbot.2017.00045. eCollection 2017.

To obtain the online solution of complex-valued systems of linear equation in complex domain with higher precision and higher convergence rate, a new neural network based on Zhang neural network (ZNN) is investigated in this paper. First, this new...

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Ding L, Xiao L, Liao B, et al. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking. Front Neurorobot. 2017;11:45doi: 10.3389/fnbot.2017.00045.
Ding, L., Xiao, L., Liao, B., Lu, R., & Peng, H. (2017). An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking. Frontiers in neurorobotics, 1145. https://doi.org/10.3389/fnbot.2017.00045
Ding, Lei, et al. "An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking." Frontiers in neurorobotics vol. 11 (2017): 45. doi: https://doi.org/10.3389/fnbot.2017.00045
Ding L, Xiao L, Liao B, Lu R, Peng H. An Improved Recurrent Neural Network for Complex-Valued Systems of Linear Equation and Its Application to Robotic Motion Tracking. Front Neurorobot. 2017 Sep 01;11:45. doi: 10.3389/fnbot.2017.00045. eCollection 2017. PMID: 28919855; PMCID: PMC5585159.
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Communication and Inference of Intended Movement Direction during Human-Human Physical Interaction.

Frontiers in neurorobotics

Mojtahedi K, Whitsell B, Artemiadis P, Santello M.
PMID: 28450834
Front Neurorobot. 2017 Apr 13;11:21. doi: 10.3389/fnbot.2017.00021. eCollection 2017.

Of particular interest to the neuroscience and robotics communities is the understanding of how two humans could physically collaborate to perform motor tasks such as holding a tool or moving it across locations. When two humans physically interact with...

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Mojtahedi K, Whitsell B, Artemiadis P, et al. Communication and Inference of Intended Movement Direction during Human-Human Physical Interaction. Front Neurorobot. 2017;11:21doi: 10.3389/fnbot.2017.00021.
Mojtahedi, K., Whitsell, B., Artemiadis, P., & Santello, M. (2017). Communication and Inference of Intended Movement Direction during Human-Human Physical Interaction. Frontiers in neurorobotics, 1121. https://doi.org/10.3389/fnbot.2017.00021
Mojtahedi, Keivan, et al. "Communication and Inference of Intended Movement Direction during Human-Human Physical Interaction." Frontiers in neurorobotics vol. 11 (2017): 21. doi: https://doi.org/10.3389/fnbot.2017.00021
Mojtahedi K, Whitsell B, Artemiadis P, Santello M. Communication and Inference of Intended Movement Direction during Human-Human Physical Interaction. Front Neurorobot. 2017 Apr 13;11:21. doi: 10.3389/fnbot.2017.00021. eCollection 2017. PMID: 28450834; PMCID: PMC5390012.
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Morphological Properties of Mass-Spring Networks for Optimal Locomotion Learning.

Frontiers in neurorobotics

Urbain G, Degrave J, Carette B, Dambre J, Wyffels F.
PMID: 28396634
Front Neurorobot. 2017 Mar 27;11:16. doi: 10.3389/fnbot.2017.00016. eCollection 2017.

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may...

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Urbain G, Degrave J, Carette B, et al. Morphological Properties of Mass-Spring Networks for Optimal Locomotion Learning. Front Neurorobot. 2017;11:16doi: 10.3389/fnbot.2017.00016.
Urbain, G., Degrave, J., Carette, B., Dambre, J., & Wyffels, F. (2017). Morphological Properties of Mass-Spring Networks for Optimal Locomotion Learning. Frontiers in neurorobotics, 1116. https://doi.org/10.3389/fnbot.2017.00016
Urbain, Gabriel, et al. "Morphological Properties of Mass-Spring Networks for Optimal Locomotion Learning." Frontiers in neurorobotics vol. 11 (2017): 16. doi: https://doi.org/10.3389/fnbot.2017.00016
Urbain G, Degrave J, Carette B, Dambre J, Wyffels F. Morphological Properties of Mass-Spring Networks for Optimal Locomotion Learning. Front Neurorobot. 2017 Mar 27;11:16. doi: 10.3389/fnbot.2017.00016. eCollection 2017. PMID: 28396634; PMCID: PMC5366341.
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Human-Derived Disturbance Estimation and Compensation (DEC) Method Lends Itself to a Modular Sensorimotor Control in a Humanoid Robot.

Frontiers in neurorobotics

Lippi V, Mergner T.
PMID: 28951719
Front Neurorobot. 2017 Sep 08;11:49. doi: 10.3389/fnbot.2017.00049. eCollection 2017.

The high complexity of the human posture and movement control system represents challenges for diagnosis, therapy, and rehabilitation of neurological patients. We envisage that engineering-inspired, model-based approaches will help to deal with the high complexity of the human posture...

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Lippi V, Mergner T. Human-Derived Disturbance Estimation and Compensation (DEC) Method Lends Itself to a Modular Sensorimotor Control in a Humanoid Robot. Front Neurorobot. 2017;11:49doi: 10.3389/fnbot.2017.00049.
Lippi, V., & Mergner, T. (2017). Human-Derived Disturbance Estimation and Compensation (DEC) Method Lends Itself to a Modular Sensorimotor Control in a Humanoid Robot. Frontiers in neurorobotics, 1149. https://doi.org/10.3389/fnbot.2017.00049
Lippi, Vittorio, and Mergner, Thomas. "Human-Derived Disturbance Estimation and Compensation (DEC) Method Lends Itself to a Modular Sensorimotor Control in a Humanoid Robot." Frontiers in neurorobotics vol. 11 (2017): 49. doi: https://doi.org/10.3389/fnbot.2017.00049
Lippi V, Mergner T. Human-Derived Disturbance Estimation and Compensation (DEC) Method Lends Itself to a Modular Sensorimotor Control in a Humanoid Robot. Front Neurorobot. 2017 Sep 08;11:49. doi: 10.3389/fnbot.2017.00049. eCollection 2017. PMID: 28951719; PMCID: PMC5599790.
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Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing.

Frontiers in neurorobotics

van der Velde F.
PMID: 27242504
Front Neurorobot. 2016 May 17;10:4. doi: 10.3389/fnbot.2016.00004. eCollection 2016.

In situ concept-based computing is based on the notion that conceptual representations in the human brain are "in situ." In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time...

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van der Velde F. Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing. Front Neurorobot. 2016;10:4doi: 10.3389/fnbot.2016.00004.
van der Velde, F. (2016). Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing. Frontiers in neurorobotics, 104. https://doi.org/10.3389/fnbot.2016.00004
van der Velde, Frank. "Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing." Frontiers in neurorobotics vol. 10 (2016): 4. doi: https://doi.org/10.3389/fnbot.2016.00004
van der Velde F. Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing. Front Neurorobot. 2016 May 17;10:4. doi: 10.3389/fnbot.2016.00004. eCollection 2016. PMID: 27242504; PMCID: PMC4869607.
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Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities.

Frontiers in neurorobotics

Zhang Y, Liao Y, Wu X, Chen L, Xiong Q, Gao Z, Zheng X, Li G, Hou W.
PMID: 29483866
Front Neurorobot. 2018 Feb 12;12:3. doi: 10.3389/fnbot.2018.00003. eCollection 2018.

So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements...

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Zhang Y, Liao Y, Wu X, et al. Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities. Front Neurorobot. 2018;12:3doi: 10.3389/fnbot.2018.00003.
Zhang, Y., Liao, Y., Wu, X., Chen, L., Xiong, Q., Gao, Z., Zheng, X., Li, G., & Hou, W. (2018). Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities. Frontiers in neurorobotics, 123. https://doi.org/10.3389/fnbot.2018.00003
Zhang, Yao, et al. "Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities." Frontiers in neurorobotics vol. 12 (2018): 3. doi: https://doi.org/10.3389/fnbot.2018.00003
Zhang Y, Liao Y, Wu X, Chen L, Xiong Q, Gao Z, Zheng X, Li G, Hou W. Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities. Front Neurorobot. 2018 Feb 12;12:3. doi: 10.3389/fnbot.2018.00003. eCollection 2018. PMID: 29483866; PMCID: PMC5816264.
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Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors.

Frontiers in neurorobotics

Everding L, Conradt J.
PMID: 29515386
Front Neurorobot. 2018 Feb 19;12:4. doi: 10.3389/fnbot.2018.00004. eCollection 2018.

In order to safely navigate and orient in their local surroundings autonomous systems need to rapidly extract and persistently track visual features from the environment. While there are many algorithms tackling those tasks for traditional frame-based cameras, these have...

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Everding L, Conradt J. Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors. Front Neurorobot. 2018;12:4doi: 10.3389/fnbot.2018.00004.
Everding, L., & Conradt, J. (2018). Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors. Frontiers in neurorobotics, 124. https://doi.org/10.3389/fnbot.2018.00004
Everding, Lukas, and Conradt, Jörg. "Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors." Frontiers in neurorobotics vol. 12 (2018): 4. doi: https://doi.org/10.3389/fnbot.2018.00004
Everding L, Conradt J. Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors. Front Neurorobot. 2018 Feb 19;12:4. doi: 10.3389/fnbot.2018.00004. eCollection 2018. PMID: 29515386; PMCID: PMC5825909.
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Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

Frontiers in neurorobotics

Atzori M, Cognolato M, Müller H.
PMID: 27656140
Front Neurorobot. 2016 Sep 07;10:9. doi: 10.3389/fnbot.2016.00009. eCollection 2016.

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of...

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Atzori M, Cognolato M, Müller H. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands. Front Neurorobot. 2016;10:9doi: 10.3389/fnbot.2016.00009.
Atzori, M., Cognolato, M., & Müller, H. (2016). Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands. Frontiers in neurorobotics, 109. https://doi.org/10.3389/fnbot.2016.00009
Atzori, Manfredo, et al. "Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands." Frontiers in neurorobotics vol. 10 (2016): 9. doi: https://doi.org/10.3389/fnbot.2016.00009
Atzori M, Cognolato M, Müller H. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands. Front Neurorobot. 2016 Sep 07;10:9. doi: 10.3389/fnbot.2016.00009. eCollection 2016. PMID: 27656140; PMCID: PMC5013051.
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Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA.

Frontiers in neurorobotics

Stratmann P, Lakatos D, Albu-Schäffer A.
PMID: 27014051
Front Neurorobot. 2016 Mar 08;10:2. doi: 10.3389/fnbot.2016.00002. eCollection 2016.

There are multiple indications that the nervous system of animals tunes muscle output to exploit natural dynamics of the elastic locomotor system and the environment. This is an advantageous strategy especially in fast periodic movements, since the elastic elements...

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Stratmann P, Lakatos D, Albu-Schäffer A. Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA. Front Neurorobot. 2016;10:2doi: 10.3389/fnbot.2016.00002.
Stratmann, P., Lakatos, D., & Albu-Schäffer, A. (2016). Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA. Frontiers in neurorobotics, 102. https://doi.org/10.3389/fnbot.2016.00002
Stratmann, Philipp, et al. "Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA." Frontiers in neurorobotics vol. 10 (2016): 2. doi: https://doi.org/10.3389/fnbot.2016.00002
Stratmann P, Lakatos D, Albu-Schäffer A. Neuromodulation and Synaptic Plasticity for the Control of Fast Periodic Movement: Energy Efficiency in Coupled Compliant Joints via PCA. Front Neurorobot. 2016 Mar 08;10:2. doi: 10.3389/fnbot.2016.00002. eCollection 2016. PMID: 27014051; PMCID: PMC4782012.
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Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System.

Frontiers in neurorobotics

Arena E, Arena P, Strauss R, Patané L.
PMID: 28337138
Front Neurorobot. 2017 Mar 08;11:12. doi: 10.3389/fnbot.2017.00012. eCollection 2017.

In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural...

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Arena E, Arena P, Strauss R, et al. Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System. Front Neurorobot. 2017;11:12doi: 10.3389/fnbot.2017.00012.
Arena, E., Arena, P., Strauss, R., & Patané, L. (2017). Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System. Frontiers in neurorobotics, 1112. https://doi.org/10.3389/fnbot.2017.00012
Arena, Eleonora, et al. "Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System." Frontiers in neurorobotics vol. 11 (2017): 12. doi: https://doi.org/10.3389/fnbot.2017.00012
Arena E, Arena P, Strauss R, Patané L. Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System. Front Neurorobot. 2017 Mar 08;11:12. doi: 10.3389/fnbot.2017.00012. eCollection 2017. PMID: 28337138; PMCID: PMC5340754.
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Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

Frontiers in neurorobotics

Wilson ED, Assaf T, Pearson MJ, Rossiter JM, Dean P, Anderson SR, Porrill J.
PMID: 26257638
Front Neurorobot. 2015 Jul 20;9:5. doi: 10.3389/fnbot.2015.00005. eCollection 2015.

The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also...

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Wilson ED, Assaf T, Pearson MJ, et al. Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum. Front Neurorobot. 2015;9:5doi: 10.3389/fnbot.2015.00005.
Wilson, E. D., Assaf, T., Pearson, M. J., Rossiter, J. M., Dean, P., Anderson, S. R., & Porrill, J. (2015). Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum. Frontiers in neurorobotics, 95. https://doi.org/10.3389/fnbot.2015.00005
Wilson, Emma D, et al. "Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum." Frontiers in neurorobotics vol. 9 (2015): 5. doi: https://doi.org/10.3389/fnbot.2015.00005
Wilson ED, Assaf T, Pearson MJ, Rossiter JM, Dean P, Anderson SR, Porrill J. Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum. Front Neurorobot. 2015 Jul 20;9:5. doi: 10.3389/fnbot.2015.00005. eCollection 2015. PMID: 26257638; PMCID: PMC4507459.
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Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

Frontiers in neurorobotics

Grinke E, Tetzlaff C, Wörgötter F, Manoonpong P.
PMID: 26528176
Front Neurorobot. 2015 Oct 13;9:11. doi: 10.3389/fnbot.2015.00011. eCollection 2015.

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt...

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Grinke E, Tetzlaff C, Wörgötter F, et al. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot. Front Neurorobot. 2015;9:11doi: 10.3389/fnbot.2015.00011.
Grinke, E., Tetzlaff, C., Wörgötter, F., & Manoonpong, P. (2015). Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot. Frontiers in neurorobotics, 911. https://doi.org/10.3389/fnbot.2015.00011
Grinke, Eduard, et al. "Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot." Frontiers in neurorobotics vol. 9 (2015): 11. doi: https://doi.org/10.3389/fnbot.2015.00011
Grinke E, Tetzlaff C, Wörgötter F, Manoonpong P. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot. Front Neurorobot. 2015 Oct 13;9:11. doi: 10.3389/fnbot.2015.00011. eCollection 2015. PMID: 26528176; PMCID: PMC4602151.
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Showing 1 to 12 of 712 entries