2θ-Burster for Rhythm-Generating Circuits

Kelley, Aaron; Shilnikov, Andrey

doi:10.3389/fams.2020.588904

Cited by 5 publications

(5 citation statements)

References 32 publications

(89 reference statements)

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“…In a FTM framework, the synaptic activity is turned “on” or “off” instantaneously. This directly utilizes the aforementioned f ∞ function with the presynaptic membrane potential V pre :

Such fast synapses happened to be useful for understanding synchronized neuronal activity, including bursting with inhibitory synapses, and multistability in small, weekly coupled neural networks ( Belykh and Shilnikov, 2008 ; Shilnikov et al, 2008 ; Jalil et al, 2010 ; Wojcik et al, 2011 ; Wojcik et al, 2014 ; Schwabedal et al, 2016 ; Collens et al, 2020 ; Kelley and Shilnikov, 2020 ; Pusuluri et al, 2020 ). Moreover, this assumption has the benefit of simplifying the network dynamics, facilitating both analysis and simulation ( Jalil et al, 2013 ; Schwabedal et al, 2014 ; Lodi et al, 2019 ).…”

Section: Methods and Modelsmentioning

confidence: 99%

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits. A tutorial

Scully,

Bourahmah,

Bloom

et al. 2024

Front. Netw. Physiol.

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In this study we focus on two subnetworks common in the circuitry of swim central pattern generators (CPGs) in the sea slugs, Melibe leonina and Dendronotus iris and show that they are independently capable of stably producing emergent network bursting. This observation raises the question of whether the coordination of redundant bursting mechanisms plays a role in the generation of rhythm and its regulation in the given swim CPGs. To address this question, we investigate two pairwise rhythm-generating networks and examine the properties of their fundamental components: cellular and synaptic, which are crucial for proper network assembly and its stable function. We perform a slow-fast decomposition analysis of cellular dynamics and highlight its significant bifurcations occurring in isolated and coupled neurons. A novel model for slow synapses with high filtering efficiency and temporal delay is also introduced and examined. Our findings demonstrate the existence of two modes of oscillation in bicellular rhythm-generating networks with network hysteresis: i) a half-center oscillator and ii) an excitatory-inhibitory pair. These 2-cell networks offer potential as common building blocks combined in modular organization of larger neural circuits preserving robust network hysteresis.

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Section: Methods and Modelsmentioning

confidence: 99%

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits. A tutorial

Scully,

Bourahmah,

Bloom

et al. 2024

Front. Netw. Physiol.

View full text Add to dashboard Cite

show abstract

“…Such fast synapses happened to be useful for understanding synchronized neuronal activity, including bursting with inhibitory synapses, and multistability in small, weekly coupled neural networks [59][60][61][62][63][64][65][66][67] . Moreover, this assumption has the benefit of simplifying the network dynamics, facilitating both analysis and simulation [68][69][70] .…”

Section: Modeling Synaptic Dynamics: From Fast To Slowmentioning

confidence: 99%

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits

Scully

Bourahmah

Bloom

et al. 2022

Preprint

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The purpose of this paper is to serve as an instructive resource and a reference catalog for biologically plausible modeling with i) conductance-based models, coupled with ii) strength varying slow synapse models, culminating in iii) two canonical pair-wise rhythm-generating networks. We document the properties of basic network components: cell models and synaptic models, which are prerequisites for proper network assembly. Using the slow-fast decomposition we present a detailed analysis of the cellular dynamics including a discussion of the most relevant bifurcations. Several approaches to model synaptic coupling are also discussed, and a new logistic model of slow synapses is introduced. Finally, we describe and examine two types of bicellular rhythm-generating networks: i) half center oscillators ii) excitatory-inhibitory pairs and elucidate a key principle - the network hysteresis underlying the stable onset of emergent slow bursting in these neural building blocks. These two cell networks are a basis for more complicated neural circuits of rhythmogenesis and feature in our models of swim central pattern generators.

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“…Such fast synapses happened to be useful for understanding synchronized neuronal activity, including bursting with inhibitory synapses, and multistability in small, weekly coupled neural networks [61][62][63][64][65][66][67][68][69]. Moreover, this assumption has the benefit of simplifying the network dynamics, facilitating both analysis and simulation [70][71][72].…”

Section: Synapse Typesmentioning

confidence: 99%

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits

Scully

Bourahmah

Bloom

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The purpose of this paper is trifold -- to serve as an instructive resource and a reference catalog for biologically plausible modeling with i) conductance-based models, coupled with ii) strength-varying slow synapse models, culminating in iii) two canonical pair-wise rhythm-generating networks. We document the properties of basic network components: cell models and synaptic models, which are prerequisites for proper network assembly. Using the slow-fast decomposition we present a detailed analysis of the cellular dynamics including a discussion of the most relevant bifurcations. Several approaches to model synaptic coupling are also discussed, and a new logistic model of slow synapses is introduced. Finally, we describe and examine two types of bicellular rhythm-generating networks: i) half-center oscillators ii) excitatory-inhibitory pairs and elucidate a key principle -- the network hysteresis underlying the stable onset of emergent slow bursting in these neural building blocks. These two cell networks are a basis for more complicated neural circuits of rhythmogenesis and feature in our models of swim central pattern generators.

show abstract

2θ-Burster for Rhythm-Generating Circuits

Cited by 5 publications

References 32 publications

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits. A tutorial

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits. A tutorial

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits

Pairing cellular and synaptic dynamics into building blocks of rhythmic neural circuits

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