Vocal production in songbirds is a key topic regarding the motor control of a complex, learned behavior. Birdsong is the result of the interaction between the activity of an intricate set of neural nuclei specifically dedicated to song production and learning (known as the “song system”), the respiratory system and the vocal organ. These systems interact and give rise to precise biomechanical motor gestures which result in song production. Telencephalic neural nuclei play a key role in the production of motor commands that drive the periphery, and while several attempts have been made to understand their coding strategy, difficulties arise when trying to understand neural activity in the frame of the song system as a whole. In this work, we report neural additive models embedded in an architecture compatible with the song system to provide a tool to reduce the dimensionality of the problem by considering the global activity of the units in each neural nucleus. This model is capable of generating outputs compatible with measurements of air sac pressure during song production in canaries (Serinus canaria). In this work, we show that the activity in a telencephalic nucleus required by the model to reproduce the observed respiratory gestures is compatible with electrophysiological recordings of single neuron activity in freely behaving animals.
REFERENCES
1
Alliende
, J. A.
, Mendez
, J. M.
, Goller
, F.
, and Mindlin
, G. B.
, “Hormonal acceleration of song development illuminates motor control mechanism in canaries
,” Dev. Neurobiol.
70
(14
), 943
–960
(2010
). 2
Alonso
, L. M.
, Alliende
, J. A.
, Goller
, F.
, and Mindlin
, G. B.
, “Low-dimensional dynamical model for the diversity of pressure patterns used in canary song
,” Phys. Rev. E Stat. Nonlin. Soft Matter Phys.
79
(4 Pt 1
), 041929
(2009
). 3
Alonso
, L. M.
, Alliende
, J. A.
, and Mindlin
, G. B.
, “Dynamical origin of complex motor patterns
,” Eur. Phys. J. D
60
(2
), 361
–367
(2010
). 4
Alonso
, R. G.
, Amador
, A.
, and Mindlin
, G. B.
, “An integrated model for motor control of song in Serinus canaria
,” J. Phys.
110
(3
), 127
–139
(2016
). 5
Alonso
, R. G.
, Trevisan
, M. A.
, Amador
, A.
, Goller
, F.
, and Mindlin
, G. B.
, “A circular model for song motor control in Serinus canaria
,” Front. Comput. Neurosci.
9
, 41
(2015
). 6
Amador
, A.
and Margoliash
, D.
, “A mechanism for frequency modulation in songbirds shared with humans
,” J. Neurosci.
33
(27
), 11136
–11144
(2013a
). 7
Amador
, A.
, Perl
, Y. S.
, Mindlin
, G. B.
, and Margoliash
, D.
, “Elemental gesture dynamics are encoded by song premotor cortical neurons
,” Nature
495
(7439
), 59
–64
(2013b
). 8
Amin
, N.
, Grace
, J. A.
, and Theunissen
, F. E.
, “Neural response to bird's own song and tutor song in the zebra finch field L and caudal mesopallium
,” J. Comp. Physiol. A Neuroethol. Sens. Neural. Behav. Physiol.
190
(6
), 469
–489
(2004
). 9
Ashmore
, R. C.
, Wild
, J. M.
, and Schmidt
, M. F.
, “Brainstem and forebrain contributions to the generation of learned motor behaviors for song
,” J. Neurosci.
25
(37
), 8543
–8554
(2005
). 10
Borisyuk
, R. M.
and Kirillov
, A. B.
, “Bifurcation analysis of a neural network model
,” Biol. Cyber.
66
(4
), 319
–325
(1992
). 11
Buzsáki
, G.
, Anastassiou
, C. A.
, and Koch
, C.
, “The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes
,” Nat. Rev. Neurosci.
13
, 407
–420
(2012
). 12
Dima
, G. C.
, Copelli
, M.
, and Mindlin
, G. B.
, “Anticipated synchronization and zero-Lag phases in population neural models
,” Int. J. Bifurc. Chaos
28
(08
), 1830025
(2018a
). 13
Dima
, G. C.
, Goldin
, M.
, and Mindlin
, G. B.
, “Modeling temperature manipulations in a circular model of birdsong production
,” Papers Phys.
10
, 100002
(2018b
). 14
Gardner
, T.
, Cecchi
, G.
, Magnasco
, M.
, Laje
, R.
, and Mindlin
, G. B.
, “Simple motor gestures for birdsongs
,” Phys. Rev. Lett.
87
(20
), 208101
(2001
). 15
Henze
, D. A.
, Borhegyi
, Z.
, Csicsvari
, J.
, Mamiya
, A.
, Harris
, K. D.
, and Buzsaki
, G.
, “Intracellular features predicted by extracellular recordings in the hippocampus in vivo
,” J. Neurophysiol.
84
(1
), 390
–400
(2000
). 16
Herbert
, C. T.
, Boari
, S.
, Mindlin
, G. B.
, and Amador
A
(2020
), “A neural population model for birdsong production
,” GitHub
, see .17
Hoppensteadt
, F. C.
and Izhikevich
, E. M.
, Weakly Connected Neural Networks
(Springer Science & Business Media
, 2012
).18
Lassa Ortiz
, J. N.
, Herbert
, C. T.
, Mindlin
, G. B.
, and Amador
, A.
, “Significant instances in motor gestures of different songbird species
,” Front. Phys.
7
, 142
(2019
). 19
Lynch
, G. F.
, Okubo
, T. S.
, Hanuschkin
, A.
, Hahnloser
, R. H.
, and Fee
, M. S.
, “Rhythmic continuous-time coding in the songbird analog of vocal motor cortex
,” Neuron
90
(4
), 877
–892
(2016
). 20
21
Mooney
, R.
, “Different subthreshold mechanisms underlie song selectivity in identified HVc neurons of the zebra finch
,” J. Neurosci.
20
, 5420
–5436
(2000
). 22
Mooney
, R.
, “Neurobiology of song learning
,” Curr. Opin. Neurobiol.
19
, 654
–660
(2009
). 23
Nottebohm
, F.
, Alvarez-Buylla
, A.
, Cynx
, J.
, Kirn
, J.
, Ling
, C. Y.
, Nottebohm
, M.
, and Williams
, H.
, “Song learning in birds: The relation between perception and production
,” Philos. Trans. R. Soc. Lond Ser. B Biol. Sci.
329
(1253
), 115
–124
(1990
). 24
Okubo
, T. S.
, Mackevicius
, E. L.
, Payne
, H. L.
, Lynch
, G. F.
, and Fee
, M. S.
, “Growth and splitting of neural sequences in songbird vocal development
,” Nature
528
(7582
), 352
–357
(2015
). 25
Ott
, E.
and Antonsen
, T. M.
, “Low dimensional behavior of large systems of globally coupled oscillators
,” Chaos
18
(3
), 037113
(2008
). 26
Picardo
, M. A.
, Merel
, J.
, Katlowitz
, K. A.
, Vallentin
, D.
, Okobi
, D. E.
, Benezra
, S. E.
, Clary
, R. C.
, Pnevmatikakis
, E. A.
, Paninski
, L.
, and Long
, M. A.
, “Population-level representation of a temporal sequence underlying song production in the zebra finch
,” Neuron
90
(4
), 866
–876
(2016
). 27
Press
, W. H.
, Teukolsky
, S. A.
, Vetterling
, W. T.
, and Flannery
, B. P.
, “Numerical recipes in C
,” Probab. in the Eng. Inf. Sci.
2
, b1
–b3
(1988
). 28
Rauske
, P. L.
, Shea
, S. D.
, and Margoliash
, D.
, “State and neuronal class-dependent reconfiguration in the avian song system
,” J. Neurophysiol.
89
, 1688
–1701
(2003
). 29
Roulet
, J.
and Mindlin
, G. B.
, “Average activity of excitatory and inhibitory neural populations
,” Chaos
26
(9
), 093104
(2016
). 30
Suthers
, R. A.
, Goller
, F.
, and Hartley
, R. S.
, “Motor stereotypy and diversity in songs of mimic thrushes
,” J. Neurobiol.
30
(2
), 231
–245
(1996
). 31
Suthers
, R. A.
and Margoliash
, D.
, “Motor control of birdsong
,” Curr. Opin. Neurobiol.
12
(6
), 684
–690
(2002
). 32
Takahashi
, D. Y.
, Fenley
, A. R.
, Teramoto
, Y.
, Narayanan
, D. Z.
, Borjon
, J. I.
, Holmes
, P.
, and Ghazanfar
, A. A.
, “The developmental dynamics of marmoset monkey vocal production
,” Science
349
(6249
), 734
–738
(2015
). 33
Trevisan
, M. A.
, Mindlin
, G. B.
, and Goller
, F.
, “Nonlinear model predicts diverse respiratory patterns of birdsong
,” Phys. Rev. Lett.
96
(5
), 058103
(2006
). 34
Tytell
, E. D.
, Holmes
, P.
, and Cohen
, A. H.
, “Spikes alone do not behavior make: Why neuroscience needs biomechanics
,” Curr. Opin. Neurobiol.
21
(5
), 816
–822
(2011
). 35
Ward
, B. C.
, Nordeen
, E. J.
, and Nordeen
, K. W.
, “Individual variation in neuron number predicts differences in the propensity for avian vocal imitation
,” Proc. Natl. Acad. Sci. U.S.A.
95
, 1277
–1282
(1998
). 36
Wilson
, H. R.
and Cowan
, J. D.
, “A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue
,” Kybernetik
13
, 55
–80
(1973
). © 2020 Author(s).
2020
Author(s)
You do not currently have access to this content.
