Books / Book Chapters

2022

• van Albada, SJ., Morales-Gregorio, A., Dickscheid T., Goulas A., Bakker R. Bludau S., Palm G., Hilgetag CC., Diesmann M. (2022) Bringing Anatomical Information into Neuronal Network Models. In: Giugliano, M., Negrello, M., Linaro, D. (eds) Computational Modelling of the Brain. Advances in Experimental Medicine and Biology 1359.
  DOI: 10.1007/978-3-030-89439-9_9
  
  
• Aertsen A., Grün S., Maldonado PE., Palm G. (eds) (2022) Introducing Computation to Neuroscience. Springer Series in Computational Neuroscience
  DOI: 10.1007/978-3-030-87447-6
  
  
• Helias M., Dahmen D. (2020) Statistical Field Theory for Neural Networks. Springer International Publishing
  DOI: 10.1007/978-3-030-46444-8
  
  
• Lawrie S., Moreno-Bote R., Gilson M. (2022) Covariance Features Improve Low-Resource Reservoir Computing Performance in Multivariate Time Series Classification. In: Smys S., Tavares JMRS., Balas VE. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing 1420, 587 - 601
  DOI: 10.1007/978-981-16-9573-5_42

2021

• van Albada S., Pronold J., van Meegen A., Diesmann M. (2021) Usage and scaling of an open-source spiking multi-area model of monkey cortex. In: Amunts K., Grandinetti L., Lippert Th., Petkov N. (eds.) Brain-Inspired Computing Cham: Springer, Lecture Notes in Computer Science 12339:47-59.
  DOI: 10.1007/978-3-030-82427-3