Polaroid non-mydriatic retinal photography.
Ray C. Paton
British medical journal
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Correlations and Synchrony Stimulus Reconstruction and Reverse Correlation Discussion: Spikes or Rates? Neuron Models Simple Spiking Neuron Model First Steps towards Coding by Spikes Threshold-Fire Models Spike Response Model -- Further Details Integrate-and-Fire Model Models of Noise Conductance-Based Models Hodgkin-Huxley Model Relation to the Spike Response Model Compartmental Models Rate Models Conclusions References Computing with Spiking Neurons Introduction A Formal Computational Model for a Network of Spiking Neurons McCulloch-Pitts Neurons versus Spiking Neurons Computing with Temporal Patterns Conincidence Detection RBF-Units in the Temporal Domain Computing a Weighted Sum in Temporal Coding Universal Approximation of Continuous Functions with Spiking Neurons Remarks: Other Computations with Temporal Patterns in Networks of Spiking Neurons Computing with a Space-Rate Code Computing with Firing Rates Computing with Firing Rates and Temporal Correlations Networks of Spiking Neurons for Storing and Retrieving Information Computing on Spike Trains Conclusions References Pulse-Based Computation in VLSI Neural Networks Background Pulsed Coding: A VLSI Perspective Pulse Amplitude Modulation Pulse Width Modulation Pulse Frequency Modulation Phase or Delay Modulation Noise, Robustness, Accuracy and Speed A MOSFET Introduction Subthreshold Circuits for Neural Networks Pulse Generation in VLSI Pulse Intercommunication Pulsed Arithmetic in VLSI Addition of Pulse Stream Signals Multiplication of Pulse Stream Signals MOS Transconductance Multiplier MOSFET Analog Multiplier Learning in Pulsed Systems Summary and Issues Raised References Encoding Information in Neuronal Activity Introduction Synchronization and Oscillations Temporal Binding Phase Coding Dynamic Range and Firing Rate Codes Interspike Interval Variability Synapses and Rate Coding Summary and Implications References Implementations Building Silicon Nervous Systems with Dendritic Tree Neuromorphs Introduction Why Spikes? Dendritic Processing of Spikes Tunability Implementation in VLSI Artificial Dendrites Synapses Dendritic Non-Linearities Spike-Generating Soma Excitability Control Spike Distribution -- Virtual Wires Neuromorphs in Action Feedback to Threshold-Setting Synapses Discrimination of Complex Spatio-Temporal Patterns Processing of Temporally Encoded Information Conclusions Acknowledgments References A Pulse-Coded Communications Infrastructure for Neuromorphic Systems Introduction Neuromorphic Computational Nodes Neuromorphic aVLSI Neurons Address Event Representation (AER) Implementations of AER Silicon Cortex Basic Layout Functional Tests of Silicon Cortex An Example Neuronal Network An Example of Sensory Input to SCX Future Research on AER Neuromorphic Systems Acknowledgements References Analog VLSI Pulsed Networks for Perceptive Processing Introduction Analog Perceptive Nets Communication Requirements Coding Information with Pulses Multiplexing of the Signals Issued by Each Neuron Non-Arbitered PFM Communication Analysis of the NAPFM Communication Systems Statistical Assumptions Detection Detection by Time-Windowing Direct Interpulse Time Measurement Performance Detection by Time-Windowing Direct Interpulse Time Measurement Data Dependency of System Performance Discussion Detection by Time-Windowing Detection by Direct Interpulse Time Measurement Address Coding Silicon Retina Equipped with the NAPFM Communication System
Hodgkin-Huxley Model • Dec 31, 2015