A reafferent and feed-forward model of song syntax generation in the Bengalese finch

Authors:
Alexander Hanuschkin;Markus Diesmann;Abigail Morrison
Affiliations:
Functional Neural Circuits Group, Faculty of Biology, Albert-Ludwig University of Freiburg, Freiburg, Germany 79104 and Bernstein Center Freiburg, Freiburg, Germany 79104;Bernstein Center Freiburg, Freiburg, Germany 79104 and Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Jülich, Jülich, Germany and ...;Functional Neural Circuits Group, Faculty of Biology, Albert-Ludwig University of Freiburg, Freiburg, Germany 79104 and Bernstein Center Freiburg, Freiburg, Germany 79104 and RIKEN Brain Science I ...
Venue:
Journal of Computational Neuroscience
Year:
2011

Citing 13
Cited 0

On numerical simulations of integrate-and-fire neural networks

Neural Computation
Controlling the Speed of Synfire Chains

ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Exact subthreshold integration with continuous spike times in discrete-time neural network simulations

Neural Computation
Spike-Timing-Dependent Plasticity in Balanced Random Networks

Neural Computation
Stdp provides the substrate for igniting synfire chains by spatiotemporal input patterns

Neural Computation
A neural network model for generating complex birdsong syntax

Biological Cybernetics
Phenomenological models of synaptic plasticity based on spike timing

Biological Cybernetics - Special Issue: Object Localization
Complex evolution of spike patterns during burst propagation through feed-forward networks

Biological Cybernetics
Developmental learning of complex syntactical song in the Bengalese finch: A neural network model

Neural Networks
Simplicity and efficiency of integrate-and-fire neuron models

Neural Computation
Comparison of local measures of spike time irregularity and relating variability to firing rate in motor cortical neurons

Journal of Computational Neuroscience
Compositionality of arm movements can be realized by propagating synchrony

Journal of Computational Neuroscience
Signal propagation in feedforward neuronal networks with unreliable synapses

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Adult Bengalese finches generate a variable song that obeys a distinct and individual syntax. The syntax is gradually lost over a period of days after deafening and is recovered when hearing is restored. We present a spiking neuronal network model of the song syntax generation and its loss, based on the assumption that the syntax is stored in reafferent connections from the auditory to the motor control area. Propagating synfire activity in the HVC codes for individual syllables of the song and priming signals from the auditory network reduce the competition between syllables to allow only those transitions that are permitted by the syntax. Both imprinting of song syntax within HVC and the interaction of the reafferent signal with an efference copy of the motor command are sufficient to explain the gradual loss of syntax in the absence of auditory feedback. The model also reproduces for the first time experimental findings on the influence of altered auditory feedback on the song syntax generation, and predicts song- and species-specific low frequency components in the LFP. This study illustrates how sequential compositionality following a defined syntax can be realized in networks of spiking neurons.