{"type":"ScholarlyArticle","title":"STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks","authors":[{"name":"Pengjie Gu","slug":"pengjie-gu","given":"Pengjie","family":"Gu","affiliation":"College of Computer Science , Sichuan University"},{"name":"Rong Xiao","slug":"rong-xiao","given":"Rong","family":"Xiao","affiliation":"College of Computer Science , Sichuan University"},{"name":"Gang Pan","slug":"gang-pan","given":"Gang","family":"Pan","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Huajin Tang","slug":"huajin-tang","given":"Huajin","family":"Tang","affiliation":"College of Computer Science , Sichuan University; College of Computer Science and Technology , Zhejiang University"}],"abstract":"The temporal credit assignment problem, which aims to discover the predictive features hidden in distracting background streams with delayed feedback, remains a core challenge in biological and machine learning. To address this issue, we propose a novel spatio-temporal credit assignment algorithm called STCA for training deep spiking neural networks (DSNNs). We present a new spatiotemporal error backpropagation policy by defining a temporal based loss function, which is able to credit the network losses to spatial and temporal domains simultaneously. Experimental results on MNIST dataset and a music dataset (MedleyDB) demonstrate that STCA can achieve comparable performance with other state-of-the-art algorithms with simpler architectures. Furthermore, STCA successfully discovers predictive sensory features and shows the highest performance in the unsegmented sensory event detection tasks.","doi":"10.24963/ijcai.2019/189","url":"https://ijcai.org/proceedings/2019/189","pdf":"https://ijcai.org/proceedings/2019/0189.pdf","markdown":"https://ijcai.org/proceedings/2019/189.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/189","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Humans and AI","track":"Main track","number":189,"pages":{"first":1366,"last":1372},"keywords":["Humans and AI: Cognitive Modeling"],"videos":[],"references":[{"position":1,"raw":"[Bittner et al., 2014] Rachel Bittner, Justin Salamon, Mike Tierney, Matthias Mauch, Chris Cannam, and Juan Bello. 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