Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Connectionist learning of belief networks
Artificial Intelligence
Pulse Stream VLSI Neural Networks
IEEE Micro
Neural Computation
Learning in stochastic bit stream neural networks
Neural Networks
A simple algorithm that discovers efficient perceptual codes
Computational and psychophysical mechanisms of visual coding
Pulse Arithmetic in VLSI Neural Networks
IEEE Micro
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This work argues that it should be possible to combine pulse-based VLSI techniques with the relatively simple training rules of the Helmholtz Machine stochastic neural architecture, in order to build an analogue probabilistic hardware model of the latter. An overview of the necessary components is presented, as well as a design for a pulse-width modulation oscillator, capable of transforming a current input (which represents the squashed, post-synaptic signal processed by a particular neuron) into the probability associated with the binary state of that neuron. A CMOS hardware prototype has been designed and fabricated, and precautions were taken during the design and simulation stages in order to prevent the oscillators on the same chip from locking together. Apart from testing the hardware prototype, future plans involve the hardware implementation of other modules, such as the synapse, the squashing function and weight changing circuitry.