Titlebook: Neuromorphic Cognitive Systems; A Learning and Memor Qiang Yu,Huajin Tang,Kay‘Tan Chen Book 2017 Springer International Publishing AG 2017

心神不宁

A Hierarchically Organized Memory Model with Temporal Population Coding,tive memory within gamma cycles. Moreover, temporally separated patterns can be linked and compressed via enhanced connections among neural groups forming episodic memory. Our model provides a computational interpretation of memory organization at a system level.

灌溉

TRAWL

Banquet

Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons,nified and consistent feedforward system network with a proper encoding scheme and supervised temporal rules is built for processing real-world stimuli. The temporal rules are used for processing the spatiotemporal patterns. To utilize these rules on images or sounds, a proper encoding method and a

一大群

A Spike-Timing Based Integrated Model for Pattern Recognition,issue of pattern recognition involving computational process from sensory encoding to synaptic learning remains underexplored, as most existing models or algorithms only target part of the computational process. Furthermore, many learning algorithms proposed in literature neglect or pay little atten

假装是我

obstinate

脆弱吧

Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections,n the network for the construction of causal connections. Synaptic efficacies are finely tuned for resulting in a desired post-synaptic firing status. Both the PSD rule and the tempotron rule are extended to multiple layers, leading to new rules of multilayer PSD (MutPSD) and multilayer tempotron (M

Kaleidoscope

A Hierarchically Organized Memory Model with Temporal Population Coding,organizing principles of memory systems remain unclear. Emerging experiment results show that memories are represented by population of neurons and organized in a categorical and hierarchical manner. In this work, we describe a hierarchically organized memory (HOM) model using spiking neurons, in wh

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