Temporal codes and recurrent timing nets for rhythmic expectancy

This paper focuses on possible time-domain neurocomputational mechanisms for short-term anticipatory processes. Here we present a simple, signal processing functional model of how short-term rhythmic pattern expectancies could be computed on the fly using recurrent neural timing nets (RTNs). The model is inspired by Gestaltist grouping principles for repeating temporal patterns of events (beats, pulses, grooves, metrical and non-metrical patterns). Building on previous autocorrelation models of pitch, meter, and rhythm, the RTN rhythm perception model consists of temporal codes, temporal pattern memory traces circulating in delay loops, and neural delay-and-coincidence networks with dynamically-adapting spike-correlation-dependent synapses. The network tracks in parallel all event periodicities in rhythmic hierarchies. As in memory trace theories of mismatch negativity (MMN-like) neural responses, it generates simple and complex pattern expectancies and registers deviations from them. Similarities and differences of this correlation-based model with those based on oscillators and predictive coding are discussed.