Pulse density Hopfield neural network system with learning capability using FPGA
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Pulse density recurrent neural network systems with learning capability using FPGA
WSEAS Transactions on Circuits and Systems
A bit-stream pulse-based digital neuron model for neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
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This paper presents a new kind of architecture for artificial digital neurons based on the voting circuit, which may be considered an improved version of those presented in literature. Stochastic pulse modulation has been used, where the values of the neuron's inputs are coded in terms of bit probabilities. The resulting activation function closely resembles the logistic sigmoid, with a transition slope that can be selected at the architectural level with no additional hardware requirements. The proposed neuron architecture has been simulated in software. Simulation results confirm that the neuron features a sigmoid transfer characteristic similar to that of conventional voting circuits. The resource occupation of the neuron, as obtained from implementation on reconfigurable platforms, has been estimated to be significantly lower than previous implementations. The theoretical analysis of the neuron's behavior is also presented.