Real time distributed processing of multiple associated pulse pattern sequences

Abstract

A Real Time Distributed Associative Memory Artificial Neural Network (RTANN) is described. This network associates groups of pulse pattern sequences. The subsequent reoccurrence of some sequences will cause the remainder to be regenerated. Training is carried out in real time simply by feeding pattern sequences directly into the network. The connections between units incorporate a wide range of transmission delays. During training the network enhances connection weights on units where coincidences occur between input and delayed pulses. Pattern regeneration utilises the reoccurrence of coincidences between delayed pulses. The simulation of an RTANN is presented. Continuous dual pattern sequences from notional sensors monitoring the shape and colour of an object were associated directly with a third dual pattern sequence having the form ‘These objects look colour’. After training the network was able to correctly generate sentences describing combinations of object and colour not encountered during training.

Introduction

The world is full of sensors! Some of them, such as frequency spectrum analysers or thermal/visual imaging devices, give a complex temporal output signal which normally requires considerable interpretation. This in turn requires a good working knowledge of the general characteristics of both the sensor calibrations and operating principles as well as the phenomena being monitored. The output from a number of these more sophisticated sensors can be in the form of a temporal series of patterns transmitted as pulses along a parallel highway similar in form to the parallel ports on many pieces of electronic equipment. An artificial neural network is described and simulated which can simultaneously accept this form of raw data from more than one sensor and associate it directly with a well defined and stable pattern sequence which is easy to interpret, display, or can be used to directly control actuators.

The network described is a Real Time Distributed Associative Memory Artificial Neural Network (RTANN). This network was developed from an aid to pattern recognition described earlier (Travis, 1982, Travis, 1984). The present RTANN now has the ability to simultaneously handle more than three input pattern sequences and also has a greatly enhanced storage capacity. A version of the network has been used to analyse data from a video camera in order to actively control the transverse intensity distribution of laser light within an aperture (Travis, 1995).

Some artificial neural networks (Hopfield, 1982, Sobajic et al., 1988, Sandler et al., 1991) process a single input pattern represented by action potentials applied to its numerous parallel inputs and give an associated output pattern represented by fixed action potentials at its parallel outputs. Networks processing a temporal sequence of patterns (Kohonen et al., 1981, Wolf, 1988, Hertz et al., 1991) receive and produce time varying action potentials at its input and output connections. Within the present primitive RTANN action potentials are restricted to two values (0 and 1). Time varying action potentials to and from the RTANN thus take on the form of pulse trains.

The basic building block of the RTANN is the section pair. This will associate a single pair of pattern sequences. The symbol used to represent a section pair is shown in Fig. 1. The section pair is trained by presenting associated pattern sequences in real time, as indicated in Fig. 1. A pair of pattern sequences has only to be presented a single time to complete the training. Many different pairs of pattern sequences can be retained simultaneously in the section pair's memory. One of these sequences can then be regenerated simply by feeding its associated sequence into the other section (Fig. 2). If more storage space is required a number of section pairs may be stacked together to form a multi-layer section pair. To associate more than two pattern sequences section pairs are linked together.

The architectural flexibility allowed by the modular design of the RTANN allows networks to be configured for specific applications. An example is shown in Fig. 3. Here the network has been specifically designed to allow the output from two notional sensors monitoring the identity and colour of an object to be associated directly with a descriptive sentence having the form ‘These objects look colour.’ The simulation of this network is presented in Section 5.