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008			170509s2017 sz ob 000 0 eng d
020			_a3319553100 _q(electronic bk.)
020			_a9783319553108 _q(electronic bk.)
020			_z3319553089
020			_z9783319553085
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050		4	_aQP357.5 _bN487 2017 EB
245	0	0	_aNeuromorphic cognitive systems : _ba learning and memory centered approach _cQiang Yu, Huajin Tang, Jun Hu, Kay Tan Chen.
264		1	_aCham _bSpringer _c2017.
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
490	0		_aIntelligent systems reference library _x1868-4394 _vvolume 126
500			_aSpringerLink
504			_aIncluye referencias bibliográficas
505	0		_aPreface; Contents; Acronyms; 1 Introduction; 1.1 Background; 1.2 Spiking Neurons; 1.2.1 Biological Background; 1.2.2 Generations of Neuron Models; 1.2.3 Spiking Neuron Models; 1.3 Neural Codes; 1.3.1 Rate Code; 1.3.2 Temporal Code; 1.3.3 Temporal Code Versus Rate Code; 1.4 Cognitive Learning and Memory in the Brain; 1.4.1 Temporal Learning; 1.4.2 Cognitive Memory in the Brain; 1.5 Objectives and Contributions; 1.6 Outline of the Book; References; 2 Rapid Feedforward Computation by Temporal Encoding and Learning with Spiking Neurons; 2.1 Introduction; 2.2 The Spiking Neural Network
505	8		_a2.3 Single-Spike Temporal Coding2.4 Temporal Learning Rule; 2.4.1 The Tempotron Rule; 2.4.2 The ReSuMe Rule; 2.4.3 The Tempotron-Like ReSuMe Rule; 2.5 Simulation Results; 2.5.1 The Data Set and the Classification Problem; 2.5.2 Encoding Images; 2.5.3 Choosing Among Temporal Learning Rules; 2.5.4 The Properties of Tempotron Rule; 2.5.5 Recognition Performance; 2.6 Discussion; 2.6.1 Encoding Benefits from Biology; 2.6.2 Types of Synapses; 2.6.3 Schemes of Readout; 2.6.4 Extension of the Network for Robust Sound Recognition; 2.7 Conclusion; References
505	8		_a3 A Spike-Timing Based Integrated Model for Pattern Recognition3.1 Introduction; 3.2 The Integrated Model; 3.2.1 Neuron Model and General Structure; 3.2.2 Latency-Phase Encoding; 3.2.3 Supervised Spike-Timing Based Learning; 3.3 Numerical Simulations; 3.3.1 Network Architecture and Encoding of Grayscale Images; 3.3.2 Learning Performance; 3.3.3 Generalization Capability; 3.3.4 Parameters Evaluation; 3.3.5 Capacity of the Integrated System; 3.4 Related Works; 3.5 Conclusions; References; 4 Precise-Spike-Driven Synaptic Plasticity for Hetero Association of Spatiotemporal Spike Patterns
505	8		_a4.1 Introduction4.2 Methods; 4.2.1 Spiking Neuron Model; 4.2.2 PSD Learning Rule; 4.3 Results; 4.3.1 Association of Single-Spike and Multi-spike Patterns; 4.3.2 Generality to Different Neuron Models; 4.3.3 Robustness to Noise; 4.3.4 Learning Capacity; 4.3.5 Effects of Learning Parameters; 4.3.6 Classification of Spatiotemporal Patterns; 4.4 Discussion and Conclusion; References; 5 A Spiking Neural Network System for Robust Sequence Recognition; 5.1 Introduction; 5.2 The Integrated Network for Sequence Recognition; 5.2.1 Rationale of the Whole System; 5.2.2 Neural Encoding Method
505	8		_a5.2.3 Item Recognition with the PSD Rule5.2.4 The Spike Sequence Decoding Method; 5.3 Experimental Results; 5.3.1 Learning Performance Analysis of the PSD Rule; 5.3.2 Item Recognition; 5.3.3 Spike Sequence Decoding; 5.3.4 Sequence Recognition System; 5.4 Discussions; 5.4.1 Temporal Learning Rules and Spiking Neurons; 5.4.2 Spike Sequence Decoding Network; 5.4.3 Potential Applications in Authentication; 5.5 Conclusion; References; 6 Temporal Learning in Multilayer Spiking Neural Networks Through Construction of Causal Connections; 6.1 Introduction; 6.2 Multilayer Learning Rules
520	3		_aThis book presents neuromorphic cognitive systems from a learning and memory-centered perspective. It illustrates how to build a system network of neurons to perform spike-based information processing, computing, and high-level cognitive tasks. It is beneficial to a wide spectrum of readers, including undergraduate and postgraduate students and researchers who are interested in neuromorphic computing and neuromorphic engineering, as well as engineers and professionals in industry who are involved in the design and applications of neuromorphic cognitive systems, neuromorphic sensors and processors, and cognitive robotics. The book formulates a systematic framework, from the basic mathematical and computational methods in spike-based neural encoding, learning in both single and multi-layered networks, to a near cognitive level composed of memory and cognition. Since the mechanisms for integrating spiking neurons integrate to formulate cognitive functions as in the brain are little understood, studies of neuromorphic cognitive systems are urgently needed. The topics covered in this book range from the neuronal level to the system level. In the neuronal level, synaptic adaptation plays an important role in learning patterns. In order to perform higher-level cognitive functions such as recognition and memory, spiking neurons with learning abilities are consistently integrated, building a system with encoding, learning and memory functionalities. The book describes these aspects in detail.
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017D
650		7	_aNeurociencia computacional _2embne _0(OCoLC)fst00872004 _0 _9481390
700	1		_aYu, Qiang.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-55310-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2018 _dz _e- _zSI