We present a neural network that is capable of completing and correcting a spiking pattern given only a partial, noisy version. It operates in continuous time and represents information using the relative timing of individual spikes. The network is capable of correcting and recalling multiple patterns simultaneously. We analyse the network's performance in terms of information recall. We explore two measures of the capacity of the network, one that values the accurate recall of individual spike

more »

... times, and another that values only the presence or absence of complete patterns. Both measures of information are found to scale linearly in both the number of neurons and the period of the patterns, suggesting these are natural measures of network information. We show that there is a smooth transition from encodings which provide precise spike times to flexible encodings that can encode many scenes. This makes it plausible that many diverse tasks could be learnt with such an encoding.