Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P. Confe

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Towards an Accurate Identification of Pyloric Neuron Activity with VSDin is an important technique to supplement electrophysiological recordings. However, utilising the technique to identify pyloric neurons directly is a computationally exacting task that requires the development of sophisticated signal processing procedures to analyse the tri-phasic pyloric patterns g

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https://doi.org/10.1007/978-3-322-91075-2ensory-independent task requires the robot to ignore irrelevant sensory information. Experimental results demonstrate that a robot controlled by our proposed method exhibits flexible and robust behavior, which results from dynamic modulation of the network input on the basis of the estimated uncertainty of actual sensory states.

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Mixing Actual and Predicted Sensory States Based on Uncertainty Estimation for Flexible and Robust Rensory-independent task requires the robot to ignore irrelevant sensory information. Experimental results demonstrate that a robot controlled by our proposed method exhibits flexible and robust behavior, which results from dynamic modulation of the network input on the basis of the estimated uncertainty of actual sensory states.

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Conference proceedings 2017, held in Alghero, Italy, in September 2017...The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions. They were organized in topical sections named: From Perception to Action; From Neurons to Networks; Brain Imaging; Recurrent Neural Networks; Neuromorp

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https://doi.org/10.1007/978-3-322-91075-2model is able to consistently outperform the baseline and achieve fully-supervised baseline performance with only 75% of all labels which demonstrates that the model is capable of using unsupervised data as an effective regulariser.

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https://doi.org/10.1007/978-3-322-90299-3ent of items in the room, and (3) attribute different labels to the same area, when approached from different angles. Analysis of the internal representations of the model showed that a topological structure corresponding to the room structure was self-organized as the trajectory of the internal activations of the network.

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https://doi.org/10.1007/978-3-322-90299-3ing synaptic plasticity on two different exemplary grasp types (pinch and cylinder). We evaluate the performance of the network in simulation and on a real anthropomorphic robotic hand. The network exposes the ability of learning finger coordination and synergies between joints that can be used for grasping.