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Artificial Neural Networks and Machine Learning – ICANN 2023
The 10-volume set LNCS 14254-14263 constitutes the proceedings of the 32nd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2023, which took place in Heraklion, Crete, Greece, during September 26–29, 2023. The 426 full papers and 9 short papers included in these proceedings were carefully reviewed and selected from 947 submissions. ICANN is a dual-track conference, featuring tracks in brain inspired computing on the one hand, and machine learning on the other, with strong cross-disciplinary interactions and applications.
Evolving BCI Therapy
As a strategic response to cognitive and CNS impairments, BCI is a theoretical outgrowth of several generations of endogenous devices for peripheral nerves, which have as a prime goal the direct replacement of lost neural function. In these earlier applications therapeutic intervention has been premised only on the restoration of signal generating capacity where nerve transmission is largely unidirectional and temporally sequenced. It is increasingly apparent, however, that the brain not only employs a very different type of syntax from that of peripheral nerves but also structures the semantic content of motor activity, fundamentally altering the conception of BCI as a therapeutic medium. The book presented here documents this change, proposing a multi-faceted strategy in which BCI therapy can restore the loss of multi-tiered, brain based motor function.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023
The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learnin...
