This letter quantitiatively investigates the effect of a temporary refractory period or dead time in the ability of a stochastic Bernoulli processor to record subsequent pulse events, following the arrival of a pulse. These effects can arise in either the input detectors of a stochastic neural network or in subsequent processing. A transient period is observed, which increases with both the dead time and the Bernoulli probability of the dead-time free system, during which the system reaches equilibrium. Unless the Bernoulli probability is small compared to the inverse of the dead time, the mean and variance of the pulse count distributions are both appreciably reduced.

We extend, in two major ways, earlier work in which sigmoidal neural nonlinearities were implemented using stochastic counters. 1) We define the signal to noise limitations of unipolar and bipolar stochastic arithmetic and signal processing. 2) We generalize the use of stochastic counters to include neural transfer functions employed in Gaussian mixture models. The hardware advantages of (nonlinear) stochastic signal processing (SSP) may be offset by increased processing time; we quantify these issues. The ability to realize accurate Gaussian activation functions for neurons in pulsed digital networks using simple hardware with stochastic signals is also analyzed quantitatively.

Examines the application of four competitive learning algorithms to the clustering of simple visual motion for use in the vision system of autonomous mobile robots. The arrangement and properties of the optical sensors used were loosely based on the visual apparatus of a jumping spider. It was found that competitive learning and specifically frequency sensitive competitive learning is able to learn to identify motion in an unsupervised manner. These learned visual representations can then be combined in subsequent processing stages for the development of active robotic vision systems. The unpredictability of a robot's operating environment and the inherent variations in the properties of physical sensors makes the use of adaptive clustering techniques essential. Both simulated and empirical results involving a modest robot demonstrate that novel motion and stationary position can be expressed as a combination of basic learned motion vectors.

The task of providing robust vision for autonom ous mobile robots is a complex signal processing problem which cannot be solved using traditional deterministic computing techniques.In this article we investigate four unsupervised neural lear ning algor ithms, known collectively as competitive learning, in order to assess both their theoretical operation and their ability to lear n to represent a basic robotic vision task.This task involves the ability of a modest robotic system to identify the components of basic motion and to generalize upon that lear ned knowledge to classify correctly novel visual experiences.This investigation shows that standard competitive lear ning and the DeSieno version of frequency-sensitive competitive lear ning (FSCL) are unsuitable for solving this problem.Soft competitive lear ning , while capable of producing an appropriate solution, is too computationally expensive in its present form to be used under the constraints of this application.However, the Krishnamur thy version of FSC L is found to be both computationally eý cient and capable of reliably lear ning a suitable solution to the motion identi® cation problem both in simulated tests and in actual hardware-based experiments.

This paper begins with an overview of several competitive learning algorithms in artificial neural networks, including self-organizing feature maps, focusing on properties of these algorithms important to hardware implementations. We then discuss previously reported digital implementations of these networks. Finally, we report a reconfigurable parallel neurocomputer architecture we have designed using digital signal processing chips and field-programmable gate array devices. Communications are based upon a broadcast network with FPGA-based message preprocessing and postprocessing. A small prototype of this system has been constructed and applied to competitive learning in self-organizing maps. This machine is able to model slowly-varying nonstationary data in real time.

The fabrication of silicon versions of artificial neural learning algorithms in existing VLSI processes introduces a variety of concerns which do not exist in a theoretical system. These include such well known circuit properties as noise, variations and nonlinearity of fabricated devices, arithmetic inaccuracy, and capacitive decay. The supervised learning algorithm-contrastive Hebbian learning, and unsupervised soft competitive learning have demonstrated their resiliency in the presence of these effects as observed in 1.2 /spl mu/m CMOS circuits employing Gilbert multipliers. It has been found that the learning circuits will operate correctly in the presence of offset errors in analog multipliers and adders, if thresholding is applied when performing weight updates.