Neuromorphic systems are implementations in silicon of sensory and neural systems whose architecture and design are based on neurobiology. This growing area offers exciting possibilities, such as sensory systems that can compete with human senses and pattern recognition systems that can run in real time. It is at the intersection of neurophysiology, computer science and electrical engineering.This book brings together recent developments in Europe and the US, so that researchers in both academia and industry can find out about the state of the art. As well as elementary material on what neuromorphic systems are and why they are growing in importance, the book contains details of current work. There are articles on aspects of implementing sensory neuromorphic systems, and also on neuromorphic hardware.Contents: Neuromorphic Systems and Theory:Neuromorphic Systems, Neural Models and Silicon (L S Smith & A Hamilton)Neuromorphism or Pragmatism? A Formal Approach (C Collin & R Woodburn)Associative Memory with Networks of Spiking Neurons in Temporal Coding (W Maass & T Natschläger)Online Clustering with Spiking Neurons Using Temporal Neurons (T Natschläger & B Ruf)Sensory Neuromorphic Systems:Analog VLSI Model of Locust DCMD Neuron Response for Computation of Object Approach (G Indiveri)Adaptive Processing Schemes Inspired by Binaural Unmasking for Enhancement of Speech Corrupted with Noise and Reverberation (P Shields et al.)Binaural Sub-Band Adaptive Speech Enhancement Using a Human Cochlear Model and Artificial Neural Networks (A Hussain & D R Campbell)Digital Hardware Implementation of Neuromorphic Pitch Extraction System (S C Lim et al.)A Paced Analog Silicon Model of Auditory Attention (T P Zahn et al.)Robot Neuroscience: A Cybernetics Approach (K Dautenhahn et al.)Neuromorphic Sensory-Motor Mobile Robot Controller with Pre-attention Mechanism (M Maris & M Mahowald)Controller for a Four-Legged Walking Machine (S Still & M W Tilden)Neuromorphic Hardware:Neuromorphic and Digital Hybrid Systems (R Etienne-Cummings et al.)Characterization of a Pyramidal Silicon Neuron (C Rasche et al.)A Strong Winner-Take-All Neural Network in Analogue Hardware (R Möller et al.)Weight Vector Normalization in an Analog VLSI Artificial Neuron Using a Backpropagating Action Potential (P Häfliger & M Mahowald)Analog VLSI Implementation of a Relaxation Oscillator for Neuromorphic Networks (J Cosp et al.)A Mixed-Mode VLSI Implementation of Grassfire Transformation (M Oláh et al.)A Hybrid (Hardware/Software) Approach Towards Implementing Hebbian Learning in Silicon Neurons with Passive Dendrites (W C Westerman et al.)Analogue VLSI Integrate and Fire Neural Network for Clustering Onset and Offset Signals in a Sound Segmentation System (M A Glover et al.)Simulation of Sparse Random Networks on a CNAPS SIMD Neurocomputer (P Paschke & R Möller)Readership: Researchers in neural networks, electrical & electronic engineering, biomedical engineering, image processing and robotics.Key Features:Only book in the market covering new topics in natural language parsing under a unified formal frameworkResults on parsing schemata are presented in major conferences in the computational linguistics fieldContributions encompass and relate parsers for both constituency and dependency representations of syntaxFormal models and proofs are shown together with empirical results