SNCC 2017 - 2017 IEEE Symposium on Neuromorphic Cognitive Computing (IEEE SNCC'17)

2017 IEEE Symposium on Neuromorphic Cognitive Computing (SNCC '17) will be held in Honolulu, Hawaii, USA from Nov. 27 to Dec 1, 2017. SNCC '17 will be a part of the IEEE Computational Intelligence Society (CIS) flagship conference, IEEE Symposium Series on Computational Intelligence 2017 (SSCI 2017). In recent years neuromorphic computing and neuromorphic engineering have become important emerging research areas. There has been rapid progress in various computational theories, learning algorithms, signal processing algorithm, circuit designs and implementations, which have shown appealing computational advantages over conventional approaches. Emulating the computational principles and architecture found in neural systems, neuromorphic cognitive computing studies sensory coding, synaptic computing, dendritic computing, unsupervised and supervised spike based learning rule, and high level of memory and cognition. The computational models and algorithms will be of fundamental importance to develop neuromorphic sensors, processors, and as well as sensory motor systems for robotic agents. The symposium will provide an important platform for the researchers across all related fields to exchange the ideas and report recent progress, and aims to bring new theoretical and technical advances in neuromorphic cognitive computing, neuromorphic engineering, and in turn to benefit highly demanded applications across machine learning, cognitive devices, neuromorphic circuits design and robotics, etc.
Topics
Neuromorphic computing
Cognitive computing
Spiking neural networks (SNNs)
Supervised and unsupervised learning
STDP
Neuromorphic visual and auditory processing
Brain-inspired data representation
Spike-based learning
Neural coding
Event based processing
Brain inspired computing for sound, vision and speech
Hardware implementation of SNNs
Deep learning for SNNs
FPGA or GPU accelerator for SNNs
Neuromorphic sensors and hardware systems
Cognitive devices and robots
SNN applications in machine learning and big data
SNN applications in BCI
SNN applications in Cyborg Intelligence