ABSTRACT
Potential strategies for temporal neural processing in the brain and their implications for the design of artificial neural networks are considered. Current connectionist thinking holds that neurons send signals to each other by changes in their average rate of discharge. This implies that there is one output signal per neuron at any given time (scalar coding), and that all neuronal specificity is achieved solely by patterns of synaptic connections. However, information can be carried by temporal codes, in temporal patterns of neural discharges and by relative times of arrival of individual spikes. Temporal coding permits multiplexing of information in the time domain, which potentially increases the flexibility of neural networks. A broadcast model of information transmission is contrasted with the current notion of highly specific connectivity. Evidence for temporal coding in somatoception, audition, electroception, gustation, olfaction and vision is reviewed, and possible neural architectures for temporal information processing are discussed.
