Neuromorphic cognitive systems : a learning and memory centered approach

• Other Authors: Yu, Qiang
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer, 2017.
• Series: Intelligent systems reference library ; v. 126.
• Subjects: Computational neuroscience.; elektronické knihy; electronic books
• ISBN: 9783319553108; 9783319553085
• ISSN: 1868-4394 ;
• Physical Description: 1 online resource

This 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, includi...

Table of Contents:

• Preface; 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.
• 2.3 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.
• 3 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.
• 4.1 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.
• 5.2.3 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.