This paper presents DENN, a dynamic neural network or neural substrate having a number of abilities that might allow it to play a useful role as a constituent of an artificial cognitive system, handling the task of low-level perceptual processing. DENN can adapt without supervision to new objects, is able to respond to patterns of activation from several objects presented simultaneously to it, and is able to automatically switch its perception between multiple objects. It is based on an ideal neural substrate as conjectured by Dimond (1980), having the twin capabilities of autonomous learning and memory, capabilities emerging due to the use of autonomous neurons. DENN has a pyramidal architecture and its neurons have topologically organized receptive fields. Through training, the neurons become feature detectors, with the higher level neurons responding to more complex features. The neurons respond to a retinal input with an oscillatory output whose frequency depends only on their own input. Due to developing phase differences, the higher level neurons can move out of phase relative to each other. Therefore, different inputs are recognized cyclically-a process we term 'automatic perception switching'. Experiments verified the system's ability of automatic perception switching, investigated its response to randomized images, and compared the performance of adaptive and non-adaptive versions of the neural substrate.