dc.contributor.author
Singh, Ishita
dc.date.accessioned
2025-01-23T07:27:43Z
dc.date.available
2025-01-23T07:27:43Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/46278
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-45990
dc.description.abstract
Neuroplatform is a research platform created by FinalSpark which offers biological ‘minibrains’ or neurospheres built on top of multi-electrode-arrays to be used for Wetware Computing. This thesis explores strategies for reinforcement learning on a mini-brain using which it ‘learns’ to adapt itself towards a target. Several computation methods are therefore used to analyse the events in the mini-brain in order to develop a quantifiable learning algorithm.
en
dc.format.extent
vii, 54 Seiten
dc.rights.uri
http://www.fu-berlin.de/sites/refubium/rechtliches/Nutzungsbedingungen
dc.subject
reinforcement learning
en
dc.subject
machine learning
en
dc.subject
biological neural networks
en
dc.subject
wetware computing
en
dc.subject
principle component analysis
en
dc.subject.ddc
500 Natural sciences and mathematics::500 Natural sciences::500 Natural sciences and mathematics
dc.subject.ddc
600 Technology, Medicine, Applied sciences::600 Technology::600 Technology, Medicine, Applied sciences
dc.subject.ddc
000 Computer science, information, and general works::000 Computer Science, knowledge, systems::000 Computer science, information, and general works
dc.title
Development of Machine Learning strategies for programming in vitro Biological Neural Networks
dc.identifier.urn
urn:nbn:de:kobv:188-refubium-46278-9
refubium.affiliation
Mathematik und Informatik
refubium.affiliation.other
Institut für Informatik
refubium.resourceType.isindependentpub
yes
dcterms.accessRights.dnb
free
dcterms.accessRights.openaire
open access
