Neuromorphic Systems Engineering Neural Networks in Silicon /

Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic syste...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Lande, Tor Sverre (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Boston, MA : Springer US, 1998.
Σειρά:The Springer International Series in Engineering and Computer Science, Analog Circuits and Signal Processing, 447
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Cochlear Systems
  • Filter Cascades as Analogs of the Cochlea
  • An Analogue VLSI Model of Active Cochlea
  • A Low-Power Wide-Dynamic-Range Analog VLSI Cochlea
  • Speech Recognition Experiments with Silicon Auditory Models
  • Retinomorphic Systems
  • The Retinomorphic Approach: Pixel-Parallel Adaptive Amplification, Filtering, and Quantization
  • Analog VLSI Excitatory Feedback Circuits for Attentional Shifts and Tracking
  • Floating-Gate Circuits for Adaptation of Saccadic Eye Movement Accuracy
  • Neuromorphic Communication
  • to Neuromorphic Communication
  • A Pulsed Communication/Computation Framework for Analog VLSI Perceptive Systems
  • Asynchronous Communication of 2D Motion Information Using Winner-Takes-All Arbitration
  • Communicating Neuronal Ensembles between Neuromorphic Chips
  • Neuromorphic Technology
  • Introduction: From Neurobiology to Silicon
  • A Low-Power Wide-Linear-Range Transconductance Amplifier
  • Floating-Gate MOS Synapse Transistors
  • Neuromorphic Synapses for Artificial Dendrites
  • Winner-Take-All Networks with Lateral Excitation
  • Neuromorphic Learning
  • Neuromorphic Learning VLSI Systems: A Survey
  • Analog VLSI Stochastic Perturbative Learning Architectures
  • Winner-Takes-All Associative Memory: A Hamming Distance Vector Quantizer.