This work addresses the question of how neural networks self-organise to recognize familiar sequential patterns. A neural network model with mild constraints on its initial architecture learns to encode the direction of spectral motion as auditory stimuli excite the units in a tonotopically arranged input layer like that found after peripheral processing by the cochlea. The network consists of a series of inhibitory clusters with excitatory interconnections that self-organize as streams of stimuli excite the clusters over time. Self-organization is achieved by application of the learning heuristics developed by Marshall (1990^ for the self-organization of excitatory and inhibitory pathways in visual motion detection. These heuristics are implemented through linear thresholding equations for unit activation having faster-than-linear inhibitory response. Synaptic weights are learned throughout processing according to the competitive algorithm explored in Malsburg (1973).

The processing of continuous acoustic signals is a challenging problem for perceptual and cognitive models. Sound patterns are usually handled by dividing the signal into long fixed-length windows—long enough for the longest patterns to be recognized. W e demonstrate a technique for recognizing acoustic patterns with a network that runs continuously in time and is fed a single spectral frame of input per network cycle. Behavior of the network in time is controlled by temporal regularities in the input patterns that allow the network to predict future events.

We propose a fully recurrent neural network to model low-level auditory memory in a task to discriminate intensities of sequentially presented tones across a range of varying interstimulus intervals. In this model, memory represents a sensory-trace of the stimulus and takes the form of slow relaxation of a number of units to a globally attractive equilibrium value near zero. The same-different judgment is based on a derivative of the output of the dynamic memory. Gaussian noise added to unit activations was found to improve the resilience of stored information although at the cost of decreased sensitivity. The model exhibits many qualitative properties of human performance on a roving-standard intensity discrimination task.