TY - CHAP
T1 - Neuromorphic computing systems based on flexible organic electronics
AU - Keene, Scott T.
AU - Gkoupidenis, Paschalis
AU - Burgt, Yoeri van de
PY - 2021
Y1 - 2021
N2 - Today software systems known as neural networks are at the basis of numerous artificial intelligence applications and are successfully implemented to translate languages, classify images, recognize diseases, and form the basis of the spur in autonomous driving. However, these algorithms require a substantial amount of computer resources and energy. The brain on the other hand, operates in a highly parallel fashion, connecting neurons via synapses, rendering it compact and highly efficient in recognizing patterns, speech, and images. Neuromorphic engineering takes advantage of the efficiency of the brain by mimicking and implementing essential concepts such as neurons and synapses in hardware. In this chapter we review the development of organic neuromorphic devices. We highlight efforts to mimic essential brain functions, such as spiking phenomena, spatiotemporal processing, homeostasis, and functional connectivity and demonstrate related applications. Next, we review important metrics for implementing low-power and reliable neuromorphic computing, such as state retention and conductance tuning. Finally, we give an outlook on future directions and potential applications, with a particular focus on interfacing with biological environments.
AB - Today software systems known as neural networks are at the basis of numerous artificial intelligence applications and are successfully implemented to translate languages, classify images, recognize diseases, and form the basis of the spur in autonomous driving. However, these algorithms require a substantial amount of computer resources and energy. The brain on the other hand, operates in a highly parallel fashion, connecting neurons via synapses, rendering it compact and highly efficient in recognizing patterns, speech, and images. Neuromorphic engineering takes advantage of the efficiency of the brain by mimicking and implementing essential concepts such as neurons and synapses in hardware. In this chapter we review the development of organic neuromorphic devices. We highlight efforts to mimic essential brain functions, such as spiking phenomena, spatiotemporal processing, homeostasis, and functional connectivity and demonstrate related applications. Next, we review important metrics for implementing low-power and reliable neuromorphic computing, such as state retention and conductance tuning. Finally, we give an outlook on future directions and potential applications, with a particular focus on interfacing with biological environments.
KW - analog memory
KW - artificial synapse
KW - brain-inspired computing
KW - global regulation phenomena
KW - hardware-implemented artificial neural networks
KW - neuromorphic engineering
KW - Organic neuromorphic systems
KW - short- and long-term plasticity
KW - spatiotemporal correlated functions
UR - http://www.scopus.com/inward/record.url?scp=85143373042&partnerID=8YFLogxK
U2 - 10.1016/B978-0-12-818890-3.00018-7
DO - 10.1016/B978-0-12-818890-3.00018-7
M3 - Chapter
AN - SCOPUS:85143373042
SN - 978-0-12-818890-3
T3 - Woodhead Publishing Series in Electronic and Optical Materials
SP - 531
EP - 574
BT - Organic Flexible Electronics
A2 - Cosseddu, Piero
A2 - Caironi, Mario
PB - Woodhead
ER -