Analog VLSI and neural systems
Analog VLSI and neural systems
VLSI implementation of neural networks
An introduction to neural and electronic networks
VLSI analogs of neuronal visual processing: a synthesis of form and function
VLSI analogs of neuronal visual processing: a synthesis of form and function
A VLSI Approach to the Implementation of Additive and Shunting Neural Networks
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Analog VLSI Circuits for Visual Motion-Based Adaptation of Post-Saccadic Drift
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
System Implementations of Analog VLSI Velocity Sensors
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
Focal-Plane and Multiple Chip VLSI Approaches to CNNs
Analog Integrated Circuits and Signal Processing - Special issue: cellular neural networks and analog VLSI
Competitive and Temporal Inhibition Structures with Spiking Neurons
Neural Processing Letters
Visual processing platform based on artificial retinas
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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The paper presents a VLSI approach to approximate thereal-time dynamics of a neuron model inspired from the classicalmodel of Hodgkin and Huxley, in which analog inputs and outputsare represented by short spikes. Both the transient and the steady-statebehaviours of these circuits depend only on process-independentlocal ratios, thus enabling single or multiple-chip VLSI implementationsof very large analog neural networks in which parallelism, asynchronyand temporal interactions are kept as important neural processingfeatures. Measurements on an integrated CMOS prototype confirmexperimentally the expected electrical and temporal behavioursof the proposed neural circuits and illustrate some outstandingfunctional features of the neural model: spike-mediated modulationof the neural activity, self-regulation of the total activityin neural groups, and emulation of temporal interaction mechanismswith well controlled time constants at different scales.