Cellular switches orchestrate rhythmic circuits

Central pattern generators; Mathematical modeling; Neuromodulation; Action Potentials; Animals; Brachyura; Computer Simulation; Models, Neurological; Nerve Net; Neurons; Nonlinear Dynamics; Periodicity; Computer science; Cybernetics; Mathematical models; Cellular mechanisms; Central pattern generator; Computational studies; Negative conductance; Neuronal circuits; Switching mechanism; Synaptic connectivity; action potential; animal; biological model; computer simulation; nerve cell; nerve cell network; nonlinear system; periodicity; physiology; Timing circuits

Abstract :

[en] Small inhibitory neuronal circuits have long been identified as key neuronal motifs to generate and modulate the coexisting rhythms of various motor functions. Our paper highlights the role of a cellular switching mechanism to orchestrate such circuits. The cellular switch makes the circuits reconfigurable, robust, adaptable, and externally controllable. Without this cellular mechanism, the circuit rhythms entirely rely on specific tunings of the synaptic connectivity, which makes them rigid, fragile, and difficult to control externally. We illustrate those properties on the much studied architecture of a small network controlling both the pyloric and gastric rhythms of crabs. The cellular switch is provided by a slow negative conductance often neglected in mathematical modeling of central pattern generators. We propose that this conductance is simple to model and key to computational studies of rhythmic circuit neuromodulation. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Disciplines :

Engineering, computing & technology: Multidisciplinary, general & others

Author, co-author :

Drion, Guillaume ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Systèmes et modélisation

Franci, Alessio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing ; Department of Mathematics, Science Faculty, National Autonomous University of Mexico, Coyoacán, D.F., Mexico

Sepulchre, Rodolphe ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Systèmes et modélisation ; Department of Engineering, University of Cambridge, Cambridge, United Kingdom

Language :

English

Title :

Cellular switches orchestrate rhythmic circuits

Publication date :

2019

Journal title :

Biological Cybernetics

ISSN :

0340-1200

eISSN :

1432-0770

Publisher :

Springer Verlag

Volume :

113

Issue :

1-2

Pages :

71 - 82

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 04 July 2024

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Bibliography

Ashby R (1952) Design for a brain: the origin of adaptive behavior. Wiley, Oxford
Bargmann C, Marder E (2013) From the connectome to brain function. Nat Methods 10(6):483–490
Berkowitz A, Roberts A, Soffe S (2010) Roles for multifunctional and specialized spinal interneurons during motor pattern generation in tadpoles, zebrafish larvae, and turtles. Front Behav Neurosci 4:36
Bucher D, Taylor AL, Marder E (2006) Central pattern generating neurons simultaneously express fast and slow rhythmic activities in the stomatogastric ganglion. J Neurophysiol 95(6):3617–3632
Butera RJ, Rinzel J, Smith JC (1999) Models of respiratory rhythm generation in the pre-bötzinger complex. ii. Populations of coupled pacemaker neurons. J Neurophysiol 82(1):398–415
Dai Y, Jordan L (2010) Multiple patterns and components of persistent inward current with serotonergic modulation in locomotor activity-related neurons in Cfos-EGFP mice. J Neurophysiol 103(4):1712–1727
Daur N, Nadim F, Bucher D (2016) The complexity of small circuits: the stomatogastric nervous system. Curr Opin Neurobiol 41:1–7
Dethier J, Drion G, Franci A, Sepulchre R (2015) A positive feedback at the cellular level promotes robustness and modulation at the circuit level. J Neurophysiol 114:2472–2484
Dickinson P, Mecsas C, Marder E (1990) Neuropeptide fusion of two motor-pattern generator circuits. Nature 344(6262):155
Drion G, Franci A, Seutin V, Sepulchre R (2012) A novel phase portrait for neuronal excitability. PLoS ONE 7(8):e41,806
Drion G, Franci A, Dethier J, Sepulchre R (2015) Dynamic input conductances shape neuronal spiking. eNeuro. 10.1523/ENEURO.0031-14.2015
Drion G, Dethier J, Franci A, Sepulchre R (2018) Switchable slow cellular conductances determine robustness and tunability of network states. PLOS Comput Biol 14(4):e1006125
Ekman M, Derrfuss J, Tittgemeyer M, Fiebach C (2012) Predicting errors from reconfiguration patterns in human brain networks. Proc Natl Acad Sci USA 109(41):16,714–16,719
FitzHugh R (1961) Impulses and physiological states in theoretical models of nerve membrane. Biophys J 1(6):445
Franci A, Drion G, Sepulchre R (2012) An organizing center in a planar model of neuronal excitability. SIAM J Appl Dyn Syst 11(4):1698–1722
Franci A, Drion G, Seutin V, Sepulchre R (2013) A balance equation determines a switch in neuronal excitability. PLoS Comput Biol 9(5):e1003,040
Franci A, Drion G, Sepulchre R (2014) Modeling the modulation of neuronal bursting: a singularity theory approach. SIAM J Appl Dyn Syst 13(2):798–829
Franci A, Drion G, Sepulchre R (2018) Robust and tunable bursting requires slow positive feedback. J Neurophysiol 119(3):1222–1234. arXiv:1707.00664
Francis N, Winkowski D, Sheikhattar A, Armengol K, Babadi B, Kanold P (2018) Small networks encode decision-making in primary auditory cortex. Neuron 97(4):885–897
Gerstner W, Kistler W, Naud R, Paninski L (2014) Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge University Press, Cambridge
Goldman MS, Golowasch J, Marder E, Abbott LF (2001) Global structure, robustness, and modulation of neuronal models. J Neurosci 21(14):5229–5238
Golomb D, Amitai Y (1997) Propagating neuronal discharges in neocortical slices: computational and experimental study. J Neurophysiol 78(3):1199–1211
Gordon I, Whelan P (2006) Monoaminergic control of cauda-equina-evoked locomotion in the neonatal mouse spinal cord. J Neurophysiol 96(6):3122–3129
Grillner S (2003) The motor infrastructure: from ion channels to neuronal networks. Nat Rev Neurosci 4(7):573–586
Gutierrez G, O’Leary T, Marder EE (2013) Multiple mechanisms switch an electrically coupled, synaptically inhibited neuron between competing rhythmic oscillators. Neuron 77(5):845–858
Haider B, McCormick D (2009) Rapid neocortical dynamics: cellular and network mechanisms. Neuron 62(2):171–189
Harris-Warrick R (2011) Neuromodulation and flexibility in central pattern generator networks. Curr Opin Neurobiol 21(5):685–692
Harris-Warrick R, Cohen A (1985) Serotonin modulates the central pattern generator for locomotion in the isolated lamprey spinal cord. J Exp Biol 116(1):27–46
Hill A, Hooser SV, Calabrese R (2003) Half-center oscillators underlying rhythmic movements. MIT Press, Cambridge, pp 507–510
Honey C, Kötter R, Breakspear M, Sporns O (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci USA 104(24):10,240–10,245
Ijspeert A (2014) Biorobotics: Using robots to emulate and investigate agile locomotion. Science 346(6206):196–203
Ijspeert A, Crespi A, Ryczko D, Cabelguen JM (2007) From swimming to walking with a salamander robot driven by a spinal cord model. Science 315(5817):1416–1420
Jordan L, Slawinska U (2011) Modulation of rhythmic movement: control of coordination, progress in brain research, vol 188. Elsevier, Amsterdam, pp 181–195
Liu Z, Golowasch J, Marder E, Abbott L (1998) A model neuron with activity-dependent conductances regulated by multiple calcium sensors. J Neurosci 18(7):2309–2320
Liu J, Akay T, Hedlund P, Pearson K, Jordan L (2009) Spinal 5-ht7 receptors are critical for alternating activity during locomotion: in vitro neonatal and in vivo adult studies using 5-ht7 receptor knockout mice. J Neurophysiol 102(1):337–348
Marder E (2012) Neuromodulation of neuronal circuits: back to the future. Neuron 76(1):1–11
Marder E, Bucher D (2001) Central pattern generators and the control of rhythmic movements. Curr Biol 11(23):R986–R996
Marder E, Bucher D (2007) Understanding circuit dynamics using the stomatogastric nervous system of lobsters and crabs. Annu Rev Physiol 69:291–316
Marder E, O’Leary T, Shruti S (2014) Neuromodulation of circuits with variable parameters: Single neurons and small circuits reveal principles of state-dependent and robust neuromodulation. Annu Rev Neurosci 37:329–347
Marder E, Goeritz M, Otopalik A (2015) Robust circuit rhythms in small circuits arise from variable circuit components and mechanisms. Curr Opin Neurobiol 31:156–163
McLean D, Masino M, Koh I, Lindquist W, Fetcho J (2008) Continuous shifts in the active set of spinal interneurons during changes in locomotor speed. Nat Neurosci 11(12):1419–1429
Mennes M, Kelly C, Colcombe S, Castellanos F, Milham M (2012) The extrinsic and intrinsic functional architectures of the human brain are not equivalent. Cereb Cortex 23(1):223–229
Meyrand P, Simmers J, Moulins M (1991) Construction of a pattern-generating circuit with neurons of different networks. Nature 351(6321):60
Pottelbergh TV, Drion G, Sepulchre R (2018) Robust modulation of integrate-and-fire models. Neural Comput 30(4):987–1011
Prinz A, Billimoria C, Marder E (2003) Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90(6):3998–4015
Rodriguez J, Blitz D, Nusbaum M (2013) Convergent rhythm generation from divergent cellular mechanisms. J Neurosci 33(46):18,047–18,064
Rubin JE, Terman D (2004) High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model. J Comput Neurosci 16(3):211–35
Terman D, Rubin JE, Yew AC, Wilson CJ (2002) Activity patterns in a model for the subthalamopallidal network of the basal ganglia. J Neurosci 22(7):2963–76
Weimann J, Marder E (1994) Switching neurons are integral members of multiple oscillatory networks. Curr Biol 4(10):896–902
White R, Nusbaum M (2011) The same core rhythm generator underlies different rhythmic motor patterns. J Neurosci 31(32):11,484–11,494
Zhong G, Sharma K, Harris-Warrick R (2011) Frequency-dependent recruitment of V2a interneurons during fictive locomotion in the mouse spinal cord. Nat Commun 2:274