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1 Department of Physics, Harvard University, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, M.I.T., Cambridge, MA, USA
2 Institute for Neuroinformatics, UNIZH/ETHZ, Zurich, Switzerland
3 Department of Brain and Cognitive Sciences, M.I.T., Cambridge, MA, USA
4 H.H.M.I, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, M.I.T., Cambridge, MA, USA
* To whom correspondence should be addressed. E-mail: prasad{at}fas.harvard.edu.
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 HVC of songbirds: The motor neurons innervating avian vocal muscles are driven by premotor nucleus RA, which is in turn driven by nucleus HVC. Recent experiments reveal that RA-projecting HVC neurons fire just one burst per song motif [Hahnloser, Kozhevnikov, & Fee (2002) Nature 419, 65-70]. However, the function of this remarkable temporal sparseness has remained unclear. Since birdsong acquisition is a clear example of a learned and complex motor behavior, we explore here with the help of numerical and analytical techniques in a neural network model the possible role of sparse premotor neural codes in motor learning. In numerical simulations with non-linear 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 only measured 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.
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