Elsevier

Current Opinion in Neurobiology

Volume 31, April 2015, Pages 156-163
Current Opinion in Neurobiology

Robust circuit rhythms in small circuits arise from variable circuit components and mechanisms

https://doi.org/10.1016/j.conb.2014.10.012Get rights and content

Highlights

  • Analysis of small oscillatory circuits reveals variability in system parameters.

  • Circuits with different parameter sets can be robust to moderate perturbations.

  • Circuits with different parameter sets can be distinguished by extreme perturbations.

  • Correlations in conductance expression can arise from homeostatic tuning rules.

  • Degenerate circuit mechanisms can produce similar switches in circuit behavior.

Small central pattern generating circuits found in invertebrates have significant advantages for the study of the circuit mechanisms that generate brain rhythms. Experimental and computational studies of small oscillatory circuits reveal that similar rhythms can arise from disparate mechanisms. Animal-to-animal variation in the properties of single neurons and synapses may underly robust circuit performance, and can be revealed by perturbations. Neuromodulation can produce altered circuit performance but also ensure reliable circuit function.

Introduction

The central pattern generating circuits found in invertebrates have been the source of numerous fundamental insights into the generation of rhythmic motor patterns, brain oscillations [1, 2, 3, 4] and some of the synaptic mechanisms that control oscillator precision [5]. Computational and experimental studies have demonstrated that some individual neurons can generate bursts of action potentials that can drive circuit oscillations (Figure 1). In other cases, circuit oscillations arise as a consequence of synaptic connections among neurons that are themselves not bursting neurons [6, 7, 8] (Figure 1).

A wealth of data has shown that neuromodulators and modulatory neurons can reconfigure oscillatory networks, changing their frequency, phase relationships, and the functional interactions among neurons [9•, 10, 11•, 12, 13•]. Notably, neurons can switch among different rhythms, and the same neuron can be part of oscillatory circuits with very different cycle periods [14, 15, 16, 17, 18].

A more recent body of work on small rhythmic circuits has shown that circuit parameters, such as ion channel densities or synaptic strengths, can be widely variable across animals in the population yet still produce rhythmic motor patterns that are normal, or ‘good enough’ [19, 20, 21, 22•, 23••, 24, 25••, 26•]. In this review, we focus on recent work that illuminates the issues raised by variability in system components for robust rhythm generation.

Section snippets

Variability in system components across animals

Many small central pattern generating circuits have been studied for more than 40 years. This means that data have been collected from the same identified neurons and synapses over extended periods of time, without the confounds that arise when experimentalists are sampling neurons from a large population of unidentified or poorly identified neurons. Consequently, it is not an accident that work on identified neurons has generated much of what we know about animal-to-animal variability of

Variability in circuit structure revealed by perturbations

If, as now seems to be the case, each crab or leech or snail, has found through development and experience, a set of membrane and synaptic conductances that are sufficient for behavior, the question then becomes how consistently can animals with these potentially quite disparate solutions to circuit performance respond appropriately to the neuromodulation and environmental perturbations that they will routinely experience? This is a telling question, as it is certainly the case that there must

Neuromodulation can reveal variability or diminish its impact

Neuromodulators can alter the output of oscillatory circuits and motor patterns in numerous ways [11]. Most members of a population of networks with different underlying structure can respond reliably to modulators, although individuals may respond differently from the mean [7]. There are many examples of state-dependent neuromodulation that depend on history [55] or initial conditions [56, 57]. There are also examples of neuromodulators that produce what appear to be paradoxical and opposing

Degeneracy in oscillator interactions and circuit performance

Although most invertebrate central pattern generating circuits consist of small numbers of neurons, this does not mean that repeated actions that appear indistinguishable necessarily employ an invariant set of neurons. Indeed, in a recent study on the Tritonia escape swim system, optical methods showed that, while many of the neurons in the circuit show stereotyped activity from cycle-to-cycle, others are active in a much more intermittent and variable fashion [65]. This is reminiscent of

Chains of coupled oscillators to produce movement

In many animals, rhythmic movements depend on coordinated action of muscle groups in many body segments. Classical work on leech swimming [73], leech heartbeat [74], and lamprey swimming [75] was critical in posing questions of how appropriate movement could be generated by coupling of many segmental oscillators. New work seeks to understand peristaltic wave propagation in crawling Drosophila larvae [76], C. elegans [77] and in the crayfish swimmeret system [78•, 79•], and further addresses

Conclusions

Brain rhythms and oscillations can have many functions in addition to generating rhythmic movements. Nonetheless, the study of small circuits that generate rhythmic movements has revealed principles of circuit organization and function that generalize to the organization of large circuits and the mechanisms by which they combine into functional units. In particular, it is clear that circuit function can be surprisingly robust to variations in many parameters. This is fortunate, as it keeps us

Conflict of interest statement

Nothing declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

This work was supported in part by NIH grants NS17813 and MH46742.

References (79)

  • P.A. Getting

    Emerging principles governing the operation of neural networks

    Annu Rev Neurosci

    (1989)
  • A.I. Selverston et al.

    Oscillatory neural networks

    Annu Rev Physiol

    (1985)
  • F. Nadim et al.

    Inhibitory feedback promotes stability in an oscillatory network

    J Neural Eng

    (2011)
  • R. Grashow et al.

    Reliable neuromodulation from circuits with variable underlying structure

    Proc Natl Acad Sci U S A

    (2009)
  • D.M. Blitz et al.

    Modulation of circuit feedback specifies motor circuit output

    J Neurosci

    (2012)
  • E. Marder

    Neuromodulation of neuronal circuits: back to the future

    Neuron

    (2012)
  • C.I. Bargmann

    Beyond the connectome: how neuromodulators shape neural circuits

    Bioessays

    (2012)
  • P.S. Dickinson et al.

    Neuropeptide fusion of two motor-pattern generator circuits

    Nature

    (1990)
  • S.L. Hooper et al.

    Switching of a neuron from one network to another by sensory-induced changes in membrane properties

    Science

    (1989)
  • J.M. Weimann et al.

    Switching neurons are integral members of multiple oscillatory networks

    Curr Biol

    (1994)
  • D. Bucher et al.

    Central pattern generating neurons simultaneously express fast and slow rhythmic activities in the stomatogastric ganglion

    J Neurophysiol

    (2006)
  • P. Meyrand et al.

    Construction of a pattern-generating circuit with neurons of different networks

    Nature

    (1991)
  • D.J. Schulz et al.

    Variable channel expression in identified single and electrically coupled neurons in different animals

    Nat Neurosci

    (2006)
  • D.J. Schulz et al.

    Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression

    Proc Natl Acad Sci U S A

    (2007)
  • S. Temporal et al.

    Neuromodulation independently determines correlated channel expression and conductance levels in motor neurons of the stomatogastric ganglion

    J Neurophysiol

    (2012)
  • S. Temporal et al.

    Activity-dependent feedback regulates correlated ion channel mRNA levels in single identified motor neurons

    Curr Biol

    (2014)
  • B.J. Norris et al.

    Constancy and variability in the output of a central pattern generator

    J Neurosci

    (2011)
  • R.C. Roffman et al.

    Animal-to-animal variability of connection strength in the leech heartbeat central pattern generator

    J Neurophysiol

    (2012)
  • J. Golowasch

    Ionic current variability and functional stability in the nervous system

    Bioscience

    (2014)
  • M.L. Goeritz et al.

    Neuropilar projections of the anterior gastric receptor neuron in the stomatogastric ganglion of the Jonah crab, Cancer borealis

    PLoS One

    (2013)
  • J.B. Thuma et al.

    Pyloric neuron morphology in the stomatogastric ganglion of the lobster, Panulirus interruptus

    Brain Behav Evol

    (2009)
  • N. Daur et al.

    Short-term synaptic plasticity compensates for variability in number of motor neurons at a neuromuscular junction

    J Neurosci

    (2012)
  • M.S. Goldman et al.

    Global structure, robustness, and modulation of neuronal models

    J Neurosci

    (2001)
  • A.E. Tobin et al.

    Correlations in ion channel mRNA in rhythmically active neurons

    PLoS One

    (2009)
  • J.L. Ransdell et al.

    Rapid homeostatic plasticity of intrinsic excitability in a central pattern generator network stabilizes functional neural network output

    J Neurosci

    (2012)
  • J.L. Ransdell et al.

    Neurons within the same network independently achieve conserved output by differentially balancing variable conductance magnitudes

    J Neurosci

    (2013)
  • S.B. Zhao et al.

    Ionic current correlations underlie the global tuning of large numbers of neuronal activity attributes

    J Neurosci

    (2012)
  • R.L. Calabrese et al.

    Coping with variability in small neuronal networks

    Integr Comp Biol

    (2011)
  • A.A. Prinz et al.

    Similar network activity from disparate circuit parameters

    Nat Neurosci

    (2004)
  • Cited by (0)

    View full text