Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong.
Sparse neural codes have been widely observed in cortical sensory and motor areas. A striking example of sparse temporal coding is in the song-related premotor area high vocal center (HVC) of songbirds: The motor neurons innervating avian vocal muscles are driven by premotor nucleus robustus archistriatalis (RA), which is in turn driven by nucleus HVC. Recent experiments reveal that RA-projecting HVC neurons fire just one burst per song motif. However, the function of this remarkable temporal sparseness has remained unclear. Because birdsong is a clear example of a learned complex motor behavior, we explore in a neural network model with the help of numerical and analytical techniques the possible role of sparse premotor neural codes in song-related motor learning. In numerical simulations with nonlinear neurons, as HVC activity is made progressively less sparse, the minimum learning time increases significantly. Heuristically, this slowdown arises from increasing interference in the weight updates for different synapses. If activity in HVC is sparse, synaptic interference is reduced, and is minimized if each synapse from HVC to RA is used only once in the motif, which is the situation observed experimentally. Our numerical results are corroborated by a theoretical analysis of learning in linear networks, for which we derive a relationship between sparse activity, synaptic interference, and learning time. If songbirds acquire their songs under significant pressure to learn quickly, this study predicts that HVC activity, currently measured only in adults, should also be sparse during the sensorimotor phase in the juvenile bird. We discuss the relevance of these results, linking sparse codes and learning speed, to other multilayered sensory and motor systems.
Fiete
Ila R
IR
Department of Physics, Harvard University, Cambridge, MA 02138, USA.
Hahnloser
Richard H R
RH
Fee
Michale S
MS
Seung
H Sebastian
HS
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001717:Birds / Q000502:physiology*; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D016014:Linear Models; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D014828:Vocalization, Animal / Q000502:physiology*