Spiking neurons represent the latest generation of neural networks designed to be more biologically realistic than more traditional artificial neural networks. This book provides a hands-on historical perspective on the development of neural networks, and investigates state-of-the-art methods for designing spiking neural networks to solve complex engineering problems. In particular, the book discusses the trade-off between biological plausibility and computational complexity of the numerous alternative models for the electrical properties of the neuron membrane, the electro-chemical interactions at the synapse, and for implementing learning. Often there is a trade-off between the sizes of the networks that can be implemented, and the complexity of the individual neurons in the network. The book investigates the definite lack of a rationale for organising all the different models in a network sense. This investigation has culminated in the development of the fuzzy spiking neural network (FSNN), which utilises fuzzy logic as a rationale for the deployment of biological models within a spiking neural network.
Spiking neurons represent the latest generation of neural networks designed to be more biologically realistic than more traditional artificial neural networks. This book provides a hands-on historical perspective on the development of neural networks, and investigates state-of-the-art methods for designing spiking neural networks to solve complex engineering problems. In particular, the book discusses the trade-off between biological plausibility and computational complexity of the numerous alternative models for the electrical properties of the neuron membrane, the electro-chemical interactions at the synapse, and for implementing learning. Often there is a trade-off between the sizes of the networks that can be implemented, and the complexity of the individual neurons in the network. The book investigates the definite lack of a rationale for organising all the different models in a network sense. This investigation has culminated in the development of the fuzzy spiking neural network (FSNN), which utilises fuzzy logic as a rationale for the deployment of biological models within a spiking neural network.
20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and...
This book offers a comprehensive exploration of the transformative role that edge devices play in advancing Internet of Things (IoT) applications. By providing real-time processing, reduced latency,...
This book gives a systemic account of major concepts, methodologies of artificial neural networks and to present a unified frame work that makes the subject more accessible to students and...
Neural Networks and Fuzzy Systems: Theory and Applications discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems. The book includes ...
Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research...
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