Event:

When listening to music, we typically lock onto and move to a beat (1-6 Hz). Behavioral studies on such synchronization (Repp 2005) abound, yet the neural mechanisms remain poorly understood. In contrast to entrainment-based models, our formulation focuses on rhythmic event time estimation and plasticity: a neuronal beat generator that adapts its controllable frequency and phase to match the external rhythm. When the stimulus is removed the beat generator continues to produce the learned rhythm in accordance with a synchronization continuation task (SCT). Further, we trained a Recurrent Neural Network (RNN) of 500 E-I units to perform the SCT. We find in analyzing the RNN three "neuronal" subgroups with one E-I pair of subgroups, tightly synchronized together, that fire around the beat times and another I-subgroup with inter-beat activity. We’ve developed a reduced three-variable model that recapitulates the RNN’s behavior.

The talk will be given on Zoom (login details below) and streamed in the small lecture hall at LMU Biocenter B01.019.

https://lmu-munich.zoom-x.de/j/94357194570?pwd=a2hBVXZ6bmdML0hQQ2k1TUJvRFhrdz09

Meeting-ID: 943 5719 4570
Kenncode: 272404