TY  - CONF
AU  - Bouhadjar, Younes
AU  - Siegel, Sebastian
AU  - Diesmann, Markus
AU  - Waser, R.
AU  - Neftci, Emre
AU  - Wouters, Dirk J.
AU  - Tetzlaff, Tom
TI  - Bio-inspired sequence learning mechanisms and their implementation in a memristive neuromorphic hardware
SN  - 1553-734X
M1  - FZJ-2024-01347
PY  - 2023
AB  - We present a sequence learning model that explains how biological networks learn to predict upcoming elements, signal non-anticipated events, and recall sequences in response to a cue signal. The model accounts for anatomical and electrophysiological properties of cortical neuronal circuits and learns complex sequences in an unsupervised manner using known biological plasticity and homeostatic control mechanisms. We further investigate the feasibility of implementing the sequence learning model on dedicated hardware mimicking brain properties, specifically focusing on memristive crossbar arrays. Finally, we apply the model to sequence classification and anomaly detection in streams of real-world data, and discuss the role of dendritic branches for the sequence learning capacity.
T2  - Neuromorphic, Natural and Physical Computing
CY  - 25 Oct 2023 - 27 Oct 2023, Hannover (Germany)
Y2  - 25 Oct 2023 - 27 Oct 2023
M2  - Hannover, Germany
LB  - PUB:(DE-HGF)6
DO  - DOI:10.34734/FZJ-2024-01347
UR  - https://juser.fz-juelich.de/record/1022226
ER  -