Analog VLSI and neural systems
Analog VLSI and neural systems
A pulse-coded communications infrastructure for neuromorphic systems
Pulsed neural networks
Neuromorphic Systems: Engineering Silicon from Neurobiology
Neuromorphic Systems: Engineering Silicon from Neurobiology
Dynamic range and sensitivity adaptation in a silicon spiking neuron
IEEE Transactions on Neural Networks
An asynchronous spiking chaotic neuron integrated circuit
Neurocomputing
FPGA realization of the LIRA neural classifier
Optical Memory and Neural Networks
A systematic method for configuring vlsi networks of spiking neurons
Neural Computation
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We describe an improved spiking silicon neuron (SN) [6] that approximates the dynamics of ionic currents of a real nerve cell. The improved version has less circuitry and fewer parameters than previous circuits thereby improving the spiking characteristics. We describe the differential equations governing the revised circuits and use them to explain the spiking properties of the SN. We also describe how to tune the parameters efficiently to bring the neuron quickly into its correct operating regime. The new neurons are sufficiently robust for operation in large networks. We demonstrate their robustness by comparing the neuron’s frequency-current curve between different chips for the same set of parameter values.