A neural network model for generating complex birdsong syntax |
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Authors: | Kentaro Katahira Kazuo Okanoya Masato Okada |
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Institution: | (1) Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan;(2) RIKEN BSI, 2-1 Hirosawa Wako, Saitama, Japan |
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Abstract: | The singing behavior of songbirds has been investigated as a model of sequence learning and production. The song of the Bengalese
finch, Lonchura striata var. domestica, is well described by a finite state automaton including a stochastic transition of the note sequence, which can be regarded
as a higher-order Markov process. Focusing on the neural structure of songbirds, we propose a neural network model that generates
higher-order Markov processes. The neurons in the robust nucleus of the archistriatum (RA) encode each note; they are activated
by RA-projecting neurons in the HVC (used as a proper name). We hypothesize that the same note included in different chunks
is encoded by distinct RA-projecting neuron groups. From this assumption, the output sequence of RA is a higher-order Markov
process, even though the RA-projecting neurons in the HVC fire on first-order Markov processes. We developed a neural network
model of the local circuits in the HVC that explains the mechanism by which RA-projecting neurons transit stochastically on
first-order Markov processes. Numerical simulation showed that this model can generate first-order Markov process song sequences. |
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