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Journals and Conferences
The pRAM (probabilistic RAM) is a nonlinear stochastic device with neuron like behavior. The pRAM is realizable in hardware, and the third-generation VLSI pRAM chip is described. This chip is adaptive since learning algorithms have been incorporated on-chip, using reinforcement training. The pRAM chip is also adaptive with respect to the interconnections… (More)
The use of additional noise in reinforcement training of probabilistic RAMS (pRAMs) is analysed in the context of pattern recognition. Both simulations and analysis indicate the effectiveness of the approach.
This paper presents a biologically inspired, hardware-realisable spiking neuron model, which we call the Temporal Noisy-Leaky Integrator (TNLI). The dynamic applications of the model as well as its applications in Computational Neuroscience are demonstrated and a learning algorithm based on postsynaptic delays is proposed. The TNLI incorporates temporal… (More)
The probabilistic RAM (pRAM) is a hardware-realizable neural device which is stochastic in operation and highly nonlinear. Even small nets of pRAMs offer high levels of functionality. The means by which a pRAM network generalizes when trained in noise is shown and the results of this behavior are described.
Speaker identification may be employed as part of a security system requiring user authentication. In this case, the claimed identity of the user is known from a magnetic card and PIN number, for example, and an utterance is requested to confirm the identity of the user. A fast response is necessary in the confirmation phase and a fast registration process… (More)
Standard graphics systems encode pictures by assigning an address and colour attribute for each point of the object resulting in a long list of addresses and attributes. Fractal geometry enables a newer class of geometrical shapes to be used to encode whole objects, thus image compression is achieved. Compression ratios of 10,000:1 have been claimed by… (More)