Sequence, exon-intron organization, transcription and mutational analysis of prnA, the gene encoding the transcriptional activator of the prn gene cluster in Aspergillus nidulans

Rate coding and temporal coding are two extremes of the neural coding process. The concept of a stationary state corresponds to the information processing approach that views the brain as a decision maker, adopts rate coding as its main strategy and endorses the single- or few neuron approach. If information derived from sensory stimulation is used to continuously update the brain's internal representation of the world, then neural codes may change with time through learning. As a consequence, the same spike sequence may be interpreted differently (or evoke a different behavior) later in the day. This non-stationary viewpoint is embodied in the representational model of brain function that stresses learning and plasticity and employs temporal coding in neural assemblies. We argue that the switching between quasi-stable brain states as a result of learning is more relevant than the neuronal patterns, and the correlations between them, that are found during stationary states. The neural code likely resides in the activity patterns that cause this state-switching.