Top

Journal of Computational Neuroscience

Published in:

01-10-2011

Responses of a bursting pacemaker to excitation reveal spatial segregation between bursting and spiking mechanisms

Authors: Selva K. Maran, Fred H. Sieling, Kavita Demla, Astrid A. Prinz, Carmen C. Canavier

Published in: Journal of Computational Neuroscience | Issue 2/2011

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Central pattern generators (CPGs) frequently include bursting neurons that serve as pacemakers for rhythm generation. Phase resetting curves (PRCs) can provide insight into mechanisms underlying phase locking in such circuits. PRCs were constructed for a pacemaker bursting complex in the pyloric circuit in the stomatogastric ganglion of the lobster and crab. This complex is comprised of the Anterior Burster (AB) neuron and two Pyloric Dilator (PD) neurons that are all electrically coupled. Artificial excitatory synaptic conductance pulses of different strengths and durations were injected into one of the AB or PD somata using the Dynamic Clamp. Previously, we characterized the inhibitory PRCs by assuming a single slow process that enabled synaptic inputs to trigger switches between an up state in which spiking occurs and a down state in which it does not. Excitation produced five different PRC shapes, which could not be explained with such a simple model. A separate dendritic compartment was required to separate the mechanism that generates the up and down phases of the bursting envelope (1) from synaptic inputs applied at the soma, (2) from axonal spike generation and (3) from a slow process with a slower time scale than burst generation. This study reveals that due to the nonlinear properties and compartmentalization of ionic channels, the response to excitation is more complex than inhibition.

previous article Effects of conduction delays on the existence and stability of one to one phase locking between two pulse-coupled oscillators

next article New determinants of firing rates and patterns of vasopressinergic magnocellular neurons: predictions using a mathematical model of osmodetection

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

Abbott, L. F., Marder, E., & Hooper, S. L. (1991). Oscillating networks: control of burst duration by electrically coupled neurons. Neural Computation, 3, 487–497.CrossRef

Arshavsky, Y. I., Deliagina, T. G., Orlovsky, G. N., & Panchin, Y. V. (1989). Control of feeding movements in the pteropod mollusk, clione-limacina. Experimental Brain Research, 78, 387–397.

Arshavsky, Y. I., Grillner, S., Orlovsky, G. N., & Panchin, Y. V. (1991). Central generators and the spatiotemporal pattern of movements. In J. Fagard & P. H. Wolff (Eds.), The development of timing control and temporal organization in coordinated action: Invariant relative timing, rhythms, and coordination (pp. 93–115). Amsterdam: Elsevier.CrossRef

Ayers, J. L., & Selverston, A. I. (1979). Monosynaptic entrainment of an endogenous pacemaker network: a cellular mechanism for von Holst's magnet effect. Journal of Comparative Physiology, 129, 5–17.CrossRef

Benson, J. A. (1980). Burst reset and frequency control of the neuronal oscillators in the cardiac ganglion of the crab, Portunus sanguinolentus. The Journal of Experimental Biology, 87, 285–313.PubMed

Bucher, D., Johnson, C. D., & Marder, E. (2007). Neuronal morphology and neuropil structure in the stomatogastric ganglion of the lobster, Homarus americanus. The Journal of Comparative Neurology, 501(2), 185–205.PubMedCrossRef

Calabrese, R. L., & Peterson, E. L. (1983). Neural control of heartbeat in the leech, hirudo medicinalis. In A. Roberts & B. Roberts (Eds.), Neural origin of rhythmic movements (pp. 195–221). Cambridge: Cambridge Univ. Press.

Canavier, C. C., & Achuthan, S. A. (2010). Pulse coupled oscillators and the phase resetting curve. Mathematical Biosciences, 226, 77–96. [Epub ahead of print] PMID: 20460132.PubMedCrossRef

Cangiano, L., & Grillner, S. (2005). Mechanisms of rhythm generation in a spinal locomotor network deprived of crossed connections: the lamprey hemicord. The Journal of Neuroscience, 25, 923–935.PubMedCrossRef

Cheng, J., Stein, R. B., Jovanovic, K., Yoshida, K., Bennett, D. J., & Han, Y. (1998). Identification, localization, and modulation of neural networks for walking in the mudpuppy (necturus maculatus) spinal cord. The Journal of Neuroscience, 18, 4295–4304.PubMed

Dando, M. R., & Selverston, A. I. (1972). Command fibres from the supra-oesophageal ganglion to the stomatogastric ganglion in Panulirus argus. Journal of Comparative Physiology, 78, 138–175.CrossRef

Demir S. S., Butera, R. J. Jr., DeFranceschi A. A., Clark, J. W. Jr., & Byrne J. H. (1997). Phase Sensitivity and Entrainment in a Modeled Bursting Neuron. Biophysical Journal, 72, 579–594.

Dorval, A. D., Christini, D. J., & White, J. A. (2001). Real-time linux dynamic clamp: a fast and flexible way to construct virtual ion channels in living cells. Annals of Biomedical Engineering, 29, 897–907.PubMedCrossRef

Ermentrout, G. B., & Kopell, N. (1991). Multiple pulse interactions and averaging in coupled neural oscillators. Journal of Mathematical Biology, 29(3), 195–217.CrossRef

Glass, L., & Winfree, A. T. (1984). Discontinuities in phase resetting experiments. The American Journal of Physiology, 246(Regulatory Integrative Comp. Physiol. 15), R251–R258.PubMed

Goldberg, J. A., Deister, C. A., & Wilson, C. J. (2007). Response properties and synchronization of rhythmically firing dendritic neurons. Journal of Neurophysiology, 97(1), 208–219. Epub 2006 Sep 6.PubMedCrossRef

Guckenheimer, J., Gueron, S., & Harris-Warrick, R. M. (1993). Mapping the dynamics of a bursting neuron. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 341, 345–359.PubMedCrossRef

Hammond, C., Bergman, H., & Brown, P. (2007). Pathological synchronization in Parkinson's disease: networks, models and treatments. Trends in Neurosciences, 30, 357–364.PubMedCrossRef

Hartline, D. K. (1979). Pattern generation in the lobster (panulirus) stomatogastric ganglion. II. Pyloric network simulation. Biological Cybernetics, 33, 223–236.PubMedCrossRef

Hartline, D. K., & Gassie, D. V. (1979). Pattern generation in the lobster (panulirus) stomatogastric ganglion.I. Pyloric neuron kinetics and synaptic interactions. Biological Cybernetics, 33, 209–222.PubMedCrossRef

Hooper, S. L., Buchman, E., Weaver, A. L., Thuma, J. B., & Hobbs, K. H. (2009). Slow conductances could underlie intrinsic phase-maintaining properties of isolated lobster (Panulirus interruptus) pyloric neurons. The Journal of Neuroscience, 29, 1834–1845.PubMedCrossRef

Huguenard, J. R., & McCormick, D. A. (2007). Thalamic synchrony and dynamic regulation of global forebrain oscillations. Trends in Neurosciences, 30, 350–356.PubMedCrossRef

Marder, E., & Calabrese, R. L. (1996). Principles of rhythmic motor pattern generation. Physiological Reviews, 76, 687–717.PubMed

McCrea, D. A., & Rybak, I. A. (2008). Organization of mammalian locomotor rhythm and pattern generation. Brain Research Reviews, 57, 134–146.PubMedCrossRef

Miller, J. P., & Selverston, A. I. (1982). Mechanisms underlying pattern generation in lobster stomatogastric ganglion as determined by selective inactivation of identified neurons. II. Oscillatory properties of pyloric neurons. Journal of Neurophysiology, 48, 1378–1391.PubMed

Nargeot, R., Baxter, D. A., & Byrne, J. H. (1997). Contingent-dependent enhancement of rhythmic motor patterns: an in vitro analog of operant conditioning. The Journal of Neuroscience, 17, 8093–8105.PubMed

Nargeot, R., Petrissans, C., & Simmers, J. (2007). Behavioral and in vitro correlates of compulsive-like food seeking induced by operant conditioning in Aplysia. The Journal of Neuroscience, 27, 8059–8070.PubMedCrossRef

Netoff, T. I., Acker, C. D., Bettencourt, J. C., & White, J. A. (2005). Beyond two-cell networks: experimental measurement of neuronal responses to multiple synaptic inputs. Journal of Computational Neuroscience, 18(3), 287–295.PubMedCrossRef

Oprisan, S. A., Thirumalai, V., & Canavier, C. C. (2003). Dynamics from a time series: can we extract the phase resetting curve from a time series? Biophysical Journal, 84, 2919–2928.PubMedCrossRef

Oprisan, S. A., Prinz, A. A., & Canavier, C. C. (2004). Phase resetting and phase locking in hybrid circuits of one model and one biological neuron. Biophysical Journal, 87, 2283–2298.PubMedCrossRef

Preyer, A. J., & Butera, R. J. (2005). Neuronal oscillators in aplysia californica that demonstrate weak coupling in vitro. Physical Review Letters, 95(13), 138103.PubMedCrossRef

Prinz, A. A., Billimoria, C. P., & Marder, E. (2003a). Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. Journal of Neurophysiology, 90, 3998–4015.CrossRef

Prinz, A. A., Thirumalai, V., & Marder, E. (2003b). The functional consequences of changes in the strength and duration of synaptic inputs to oscillatory neurons. The Journal of Neuroscience, 23, 943–954.

Prinz, A. A., Abbott, L. F., & Marder, E. (2004). The dynamic clamp comes of age. Trends in Neurosciences, 27, 218–224.PubMedCrossRef

Rinzel, J., & Ermentrout, B. (1989). Analysis of neural excitability and oscillations. In C. Koch & I. Segev (Eds.), Methods in neuronal modelling: From synapses to networks". Cambridge: MIT. revised 1998.

Schultheiss, N. W., Edgerton, J. R., & Jaeger, D. (2010). Phase response curve analysis of a full morphological globus pallidus neuron model reveals distinct perisomatic and dendritic modes of synaptic integration. The Journal of Neuroscience, 30(7), 2767–2782.PubMedCrossRef

Sharp, A. A., O'Neil, M. B., Abbott, L. F., & Marder, E. (1993a). Dynamic Clamp: computer-generated conductances in real neurons. Journal of Neurophysiology, 69, 992–995.PubMed

Sharp, A. A., O'Neil, M. B., Abbott, L. F., & Marder, E. (1993b). The dynamic clamp—Artificial conductances in biological neurons. Trends in Neurosciences, 16, 389–394.PubMedCrossRef

Sieling, F. H., Canavier, C. C., Prinz, A. A. (2009). Predictions of phase-locking in excitatory hybrid networks: excitation does not promote phase-locking in pattern generating networks as reliably as inhibition. J Neurophysiol, 102, 69–84. First published doi:10.1152/jn.00091.2009.

Soto-Treviño, C., Rabbah, P., Marder, E., & Nadim, F. (2005). Computational model of electrically coupled, intrinsically distinct pacemaker neuron. Journal of Neurophysiology, 94, 590–604.PubMedCrossRef

Stein, S. G., Grillner, S., Selverston, A. I., & Stuart, D. G., (Eds). (1997). Neurons, networks, and motor behavior. MIT.

Tazaki, K., & Cooke, I. M. (1990). Characterization of Ca current underlying burst formation in lobster cardiac ganglion motorneurons. Journal of Neurophysiology, 63, 370–384.PubMed

Tohidi, V., & Nadim, F. (2009). Membrane resonance in bursting pacemaker neurons of an oscillatory network is correlated with network frequency. The Journal of Neuroscience, 29, 6427–6435.PubMedCrossRef

Traub, R. D., & Jefferys, J. G. (1994). Are there unifying principles underlying the generation of epileptic afterdischarges in vitro? Progress in Brain Research, 102, 383–394.PubMedCrossRef

Turrigiano, G., Abbott, L. F., & Marder, E. (1994). Activity-dependent changes in the intrinsic properties of cultured neurons. Science, 264(5161), 974–977.PubMedCrossRef

Turrigiano, G., LeMasson, G., & Marder, E. (1995). Selective regulation of current densities underlies spontaneous changes in the activity of cultured neurons. Journal of Neuroscience, 15(5 Pt 1), 3640–3652.PubMed

Winfree, A. T. (1980). The geometry of biological time. New York: Springer.

Title: Responses of a bursting pacemaker to excitation reveal spatial segregation between bursting and spiking mechanisms
Authors: Selva K. Maran
Fred H. Sieling
Kavita Demla
Astrid A. Prinz
Carmen C. Canavier
Publication date: 01-10-2011
Publisher: Springer US
Published in: Journal of Computational Neuroscience / Issue 2/2011
Print ISSN: 0929-5313
Electronic ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-011-0319-y

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 2/2011

Effects of conduction delays on the existence and stability of one to one phase locking between two pulse-coupled oscillators

The use of automated parameter searches to improve ion channel kinetics for neural modeling

Virtual NEURON: a strategy for merged biochemical and electrophysiological modeling

In vivo conditions influence the coding of stimulus features by bursts of action potentials

New determinants of firing rates and patterns of vasopressinergic magnocellular neurons: predictions using a mathematical model of osmodetection

Interactions of persistent sodium and calcium-activated nonspecific cationic currents yield dynamically distinct bursting regimes in a model of respiratory neurons

Premium Partner