Song Replay During Sleep and Computational Rules for Sensorimotor Vocal Learning

Author:

Dave Amish S.1,Margoliash Daniel1

Affiliation:

1. Department of Organismal Biology and Anatomy, University of Chicago, 1027 East 57 Street, Chicago, IL 60637, USA.

Abstract

Songbirds learn a correspondence between vocal-motor output and auditory feedback during development. For neurons in a motor cortex analog of adult zebra finches, we show that the timing and structure of activity elicited by the playback of song during sleep matches activity during daytime singing. The motor activity leads syllables, and the matching sensory response depends on a sequence of typically up to three of the preceding syllables. Thus, sensorimotor correspondence is reflected in temporally precise activity patterns of single neurons that use long sensory memories to predict syllable sequences. Additionally, “spontaneous” activity of these neurons during sleep matches their sensorimotor activity, a form of song “replay.” These data suggest a model whereby sensorimotor correspondences are stored during singing but do not modify behavior, and off-line comparison (e.g., during sleep) of rehearsed motor output and predicted sensory feedback is used to adaptively shape motor output.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference39 articles.

1. In songbirds there is a minimum delay of ∼70 ms or longer between the production of a burst of neuronal activity contributing to song generation and the reception of processed auditory input from the resulting vocalization. In zebra finches syllables are typically 50 to 200 ms in duration and may comprise one or several notes. The minimal delay is the sum of premotor lead (∼50 ms) and sensory lag. Sensory lag is the sum of the minimal latency for response (∼20 ms) and the sensory integration time which for song system neurons can be tens to hundreds of milliseconds.

2. R. S. Sutton A. G. Barto Reinforcement Learning: An Introduction (MIT Press London 1998).

3. Learning of sequential movements by neural network model with dopamine-like reinforcement signal

4. Model of Cortical-Basal Ganglionic Processing: Encoding the Serial Order of Sensory Events

5. A Computational Model of How the Basal Ganglia Produce Sequences

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