Analog hardware implementation of the random neural network model

Presents a simple continuous analog hardware realization of the random neural network (RNN) model. The proposed circuit uses the general principles resulting from the understanding of the basic properties of the firing neuron. The circuit for the neuron model consists only of operational amplifiers, transistors, and resistors, which makes it candidate for VLSI implementation of random neural networks with feedforward or recurrent structures. Although the literature is rich with various methods for implementing the different neural networks structures, the proposed implementation is very simple and can be built using discrete integrated circuits for problems that need a small number of neurons. A software package, RNNSIM, has been developed to train the RNN model and supply the network parameters which can be mapped to the hardware structure. As an assessment on the proposed circuit, a simple neural network mapping function has been designed and simulated using PSpice.

[1]  Erol Gelenbe,et al.  Stability of the Random Neural Network Model , 1990, Neural Computation.

[2]  Isik Aybay,et al.  A digital neuron realization for the random neural network model , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[3]  Zhi-Hong Mao,et al.  Function approximation with spiked random networks , 1999, IEEE Trans. Neural Networks.

[4]  Erol Gelenbe,et al.  The Random Neural Network Model for Texture Generation , 1992, Int. J. Pattern Recognit. Artif. Intell..

[5]  Erol Gelenbe,et al.  Random Neural Networks with Negative and Positive Signals and Product Form Solution , 1989, Neural Computation.

[6]  Erol Gelenbe,et al.  Low bit-rate video compression with neural networks and temporal subsampling , 1996, Proc. IEEE.

[7]  Erol Gelenbe,et al.  Learning in the Recurrent Random Neural Network , 1992, Neural Computation.

[8]  Erol Gelenbe,et al.  Random neural network decoder for error correcting codes , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).