Phase response properties of half-center oscillators

Zhang, Calvin; Lewis, Timothy J.

doi:10.1007/s10827-013-0440-1

Cited by 41 publications

(29 citation statements)

References 47 publications

(95 reference statements)

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“…Phase-resetting characteristics can be measured for a single oscillating neuron [11,12] or for network oscillators [13,14]. Figure 1 defines the phase of an oscillator and shows how it can be reset, using a simple network oscillator model [15] that consists of the average firing rates of two neural populations, one excitatory (E) and one inhibitory (I).…”

Section: Phase-resettingmentioning

confidence: 99%

Phase-resetting as a tool of information transmission

Canavier

2015

Current Opinion in Neurobiology

101

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Models of information transmission in the brain largely rely on firing rate codes. The abundance of oscillatory activity in the brain suggests that information may be also encoded using the phases of ongoing oscillations. Sensory perception, working memory and spatial navigation have been hypothesized to use phase codes, and cross-frequency coordination and phase synchronization between brain areas have been proposed to gate the flow of information. Phase codes generally require the phase of the oscillations to be reset at specific reference points for consistent coding, and coordination between oscillators requires favorable phase resetting characteristics. Recent evidence supports a role for neural oscillations in providing temporal reference windows that allow for correct parsing of phase-coded information.

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Section: Phase-resettingmentioning

confidence: 99%

Phase-resetting as a tool of information transmission

Canavier

2015

Current Opinion in Neurobiology

101

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“…The magnitude of the acceleration or deceleration of phase depends on the timing and structure of the input from the jth CPG and the state-dependent response of the ith CPG. This effect is quantified by the "interaction function," which is a function of the phase difference between the two coupled CPGs (18,35) and is related to the phase response curve (SI Text, section 2.2).…”

Section: A Robust Neural Mechanism Producing Phase Constancymentioning

confidence: 99%

Neural mechanism of optimal limb coordination in crustacean swimming

Zhang¹,

Guy²,

Mulloney

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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A fundamental challenge in neuroscience is to understand how biologically salient motor behaviors emerge from properties of the underlying neural circuits. Crayfish, krill, prawns, lobsters, and other long-tailed crustaceans swim by rhythmically moving limbs called swimmerets. Over the entire biological range of animal size and paddling frequency, movements of adjacent swimmerets maintain an approximate quarter-period phase difference with the more posterior limbs leading the cycle. We use a computational fluid dynamics model to show that this frequency-invariant stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers in crustacean swimming. We then show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Specifically, the wave-like limb coordination emerges robustly from a combination of the half-center structure of the local central pattern generating circuits (CPGs) that drive the movements of each limb, the asymmetric network topology of the connections between local CPGs, and the phase response properties of the local CPGs, which we measure experimentally. Thus, the crustacean swimmeret system serves as a concrete example in which the architecture of a neural circuit leads to optimal behavior in a robust manner. Furthermore, we consider all possible connection topologies between local CPGs and show that the natural connectivity pattern generates the biomechanically optimal stroke pattern most robustly. Given the high metabolic cost of crustacean swimming, our results suggest that natural selection has pushed the swimmeret neural circuit toward a connection topology that produces optimal behavior. locomotion | coupled oscillators | phase locking | metachronal waves I t is widely believed that neural circuits have evolved to optimize behavior that increases reproductive fitness. Despite this belief, few studies have clearly identified the neural mechanisms producing optimal behaviors. The complexity of behaviors generally makes it difficult to assess their optimality, and neural circuits are often too complicated to concretely link neural mechanisms to the overt behavior. Energy-intensive locomotion such as steady swimming, walking, and flying provides important model systems for studying optimality because the goal of the behavior is clear and it is likely to have been optimized for efficiency (1). For example, the kinematics of locomotion has been shown to be optimal in the cases of the undulatory motion of the sandfish lizard and the lamprey (2, 3). On the other hand, the neural circuits underlying locomotion in most organisms are not sufficiently characterized to understand how they give rise to the optimal motor behavior. Because of the distinct frequency-invariant stroke pattern and the relative simplicity of the neuronal circuit, limb coordination of long-tailed crustaceans during steady swimming provides an ideal model system ...

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“…There is an extensive and ongoing effort to understand the dynamics of half center oscillators in the context of central pattern generation [10, 11, 14, 16, 17]. In many cases, it has been noted that a careful coordination between network elements is necessary to generate and set the frequency of the network [18, 19, 20].…”

Section: Discussionmentioning

confidence: 99%

The role of electrical coupling in generating and modulating oscillations in a neuronal network

Mouser

Bose

Nadim

2016

Mathematical Biosciences

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A simplified model of the crustacean gastric mill network is considered. Rhythmic activity in this network has largely been attributed to half center oscillations driven by mutual inhibition. We use mathematical modeling and dynamical systems theory to show that rhythmic oscillations in this network may also depend on, or even arise from, a voltage-dependent electrical coupling between one of the cells in the half-center network and a projection neuron that lies outside of the network. This finding uncovers a potentially new mechanism for the generation of oscillations in neuronal networks.

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Phase response properties of half-center oscillators

Cited by 41 publications

References 47 publications

Phase-resetting as a tool of information transmission

Phase-resetting as a tool of information transmission

Neural mechanism of optimal limb coordination in crustacean swimming

The role of electrical coupling in generating and modulating oscillations in a neuronal network

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