Order parameter for bursting polyrhythms in multifunctional central pattern generators

Wojcik, Jeremy; Clewley, Robert; Shilnikov, Andrey

doi:10.1103/physreve.83.056209

Cited by 46 publications

(73 citation statements)

References 19 publications

(12 reference statements)

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“…The model has turned out to be very reach dynamically as can demonstrate a number of global bifurcations at the activity transitions, like the blue sky catastrophe, bi-stability and chaos due to homoclinic saddle orbits [30]. In this study, individually each post-synaptic interneuron is an endogenous burster that can become temporarily shut at the hyper-polarized state by an inhibitory current originating from pre-synaptic interneuron(s) [12,15].…”

Section: Methodsmentioning

confidence: 91%

“…In addition, the capacity of pattern generation of 3-neuron motifs made of reciprocally inhibitory interneurons is well understood [7,15].…”

Section: Cpg Dissection In Homo-and Heterogeneous Hcosmentioning

confidence: 99%

“…So, for the sake of generality, we set the parameters of the individual interneurons and the cross-coupling some different to guarantee that anti-phase bursting is the only stable pattern in either HCO [12,15]. The 3D phase space of the unit cube for the uncoupled HCOs is given in Fig.…”

Section: Cpg Dissection In Homo-and Heterogeneous Hcosmentioning

confidence: 99%

“…A whole CPG can be also multifunctional if it governs more than one behavior, in contrast to a dedicated CPG which is arranged for the purpose of a single locomotion. Modeling studies, mathematical and computational, have proven to be useful for gaining insights into operational principles of CPGs [8][9][10][11][12][13], in particular, multifunctional ones [14][15][16]. This study has been inspired by recent experimental studies of neuronal activity patterns recorded from the identified interneurons of a [dedicated] CPG governing the swim pattern of a sea slug Melibe leonina [17][18][19] and possible consequences of understanding neurological phenomena in general.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Toward robust phase-locking in Melibe swim central pattern generator models

Jalil

Allen

Youker

et al. 2013

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Small groups of interneurons, abbreviated by CPG for central pattern generators, are arranged into neural networks to generate a variety of core bursting rhythms with specific phase-locked states, on distinct time scales, that govern vital motor behaviors in invertebrates such as chewing, swimming, etc. These movements in lower level animals mimic motions of organs in higher animals due to evolutionarily conserved mechanisms. Hence, various neurological diseases can be linked to abnormal movement of body parts that are regulated by a malfunctioning CPG. In this paper, we, being inspired by recent experimental studies of neuronal activity patterns recorded from a swimming motion CPG of the sea slug Melibe leonina, examine a mathematical model of a 4-cell network that can plausibly and stably underlie the observed bursting rhythm. We develop a dynamical systems framework for explaining the existence and robustness of phase-locked states in activity patterns produced by the modeled CPGs. The proposed tools can be used for identifying core components for other CPG networks with reliable bursting outcomes and specific phase relationships between the interneurons. Our findings can be employed for identifying or implementing the conditions for normal and pathological functioning of basic CPGs of animals and artificially intelligent prosthetics that can regulate various movements. Repetitive behaviors are often associated hypothetically with the phenomenon of rhythmogenesis in small networks that are able autonomously to generate or continue, after induction a variety of activity patterns without further external input, abrupt or not. The goal of this modeling study is to identify decisive components of a biologically based CPG that has been linked to a specific motion in a lower order animal, sea slug Melibe leonina, which produces a specific bursting pattern, as well as to identify components that ensure the pattern's robustness. Due to the recurrent nature of such bursting patterns of self-sustained activity we employ Poincare return maps defined on phases and phase-lags between burst initiations in the interneurons to study quantitative and qualitative properties of CPG rhythms and corresponding attractors.The proposed approach is specifically tailored for various studies of neural networks in neuroscience, computational and experimental. Development of such tools and our understanding of such CPGs can be applied to gain insight into governing principles of neurological phenomena in higher order animals and can aid in treating anomalies associated with neurological disorders.

show abstract

Section: Methodsmentioning

confidence: 91%

“…In addition, the capacity of pattern generation of 3-neuron motifs made of reciprocally inhibitory interneurons is well understood [7,15].…”

Section: Cpg Dissection In Homo-and Heterogeneous Hcosmentioning

confidence: 99%

Section: Cpg Dissection In Homo-and Heterogeneous Hcosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Toward robust phase-locking in Melibe swim central pattern generator models

Jalil

Allen

Youker

et al. 2013

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, a great deal of computational studies have been focused on a range of 3-cell motifs of bursting neurons coupled by chemical (inhibitory and excitatory) and electrical synapses to disclose the role of coupling in generating sets of coexisting rhythmic outcomes, see Wojcik et al, 2011Schwabedal et al, 2014Schwabedal et al, , 2015Collens et al, 2015] and references therein. These network structures reflect the known physiological details of various CPG networks in real animals.…”

Section: Introductionmentioning

confidence: 99%