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J Neurophysiol 98: 2038-2057, 2007. First published July 25, 2007; doi:10.1152/jn.01311.2006
0022-3077/07 $8.00

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Model of Birdsong Learning Based on Gradient Estimation by Dynamic Perturbation of Neural Conductances

¹Kavli Institute for Theoretical Physics, University of California, Santa Barbara, Santa Barbara; ²Center for Theoretical Biological Physics, University of California, San Diego, La Jolla, California; and ³McGovern Institute for Brain Research, ⁴Howard Hughes Medical Institute, and ⁵Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, Massachusetts

Submitted 14 December 2006; accepted in final form 13 July 2007

We propose a model of songbird learning that focuses on avian brain areas HVC and RA, involved in song production, and area LMAN, important for generating song variability. Plasticity at HVC RA synapses is driven by hypothetical "rules" depending on three signals: activation of HVC RA synapses, activation of LMAN RA synapses, and reinforcement from an internal critic that compares the bird's own song with a memorized template of an adult tutor's song. Fluctuating glutamatergic input to RA from LMAN generates behavioral variability for trial-and-error learning. The plasticity rules perform gradient-based reinforcement learning in a spiking neural network model of song production. Although the reinforcement signal is delayed, temporally imprecise, and binarized, the model learns in a reasonable amount of time in numerical simulations. Varying the number of neurons in HVC and RA has little effect on learning time. The model makes specific predictions for the induction of bidirectional long-term plasticity at HVC RA synapses.

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