The Functional Consequences of Changes in the Strength and Duration of Synaptic Inputs to Oscillatory Neurons

Prinz, Astrid A.; Thirumalai, Vatsala; Marder, Eve

doi:10.1523/jneurosci.23-03-00943.2003

Cited by 118 publications

(142 citation statements)

References 47 publications

(55 reference statements)

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“…The phase-response curve (Fig.·7D) shows that AGR was capable of resetting the gastric mill rhythm independently of stimulus phase. Phase-response curves show the change in oscillator period elicited by inputs occurring at different phases in the rhythm (Prinz et al, 2003;Wolf and Pearson, 1988) and thus are a solid way of confirming the functional significance of a discrete input to an oscillatory system (Abramovich-Sivan and Akselrod, 1998). As a result of AGR resetting capabilities, rhythmic AGR stimulation entrained the rhythm (Fig.·7A).…”

Section: Effects On the Pyloric And Gastric Circuitmentioning

confidence: 82%

Sensory-induced modification of two motor patterns in the crab,Cancer pagurus

Smarandache

Stein

2007

Journal of Experimental Biology

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SUMMARY Sensory input is pervasive among motor networks and continuously adapts motor patterns to changing circumstances. To elucidate common principles of sensorimotor integration, it is beneficial to characterize sensory influences on motor network operation and compare these influences between species. To facilitate such comparison, we have studied the influence of the anterior gastric receptor (AGR) – a proprioceptor that has been characterized in detail in two lobster species – on the pyloric (filtering of food) and gastric mill (chewing of food) motor patterns in the crab Cancer pagurus. AGR has a bipolar cell body in the stomatogastric ganglion; it was activated by tension increase in gastric mill powerstroke muscles. While two spike initiation zones accounted for its spontaneous activity, active membrane properties (sag potentials, spike frequency adaptation) contributed to the AGR response to current injections. When activated, AGR diminished spike activities in two pyloric motor neurons and prolonged the pyloric cycle period. Furthermore, AGR excited gastric mill protractor neurons, inhibited the retractor neuron and evoked phase-independent resetting of the gastric mill rhythm. Repetitive spike trains entrained the rhythm to both longer and shorter cycle periods. All AGR actions seemed to be mediated via at least two premotor projection neurons in the spatially distant commissural ganglia. The response of the gastric mill neurons was independent of AGR firing frequency. Our results suggest that homologous proprioceptors can elicit similar effects on motor patterns while utilizing different mechanisms. This work thus provides an initial framework for future studies to determine underlying common principles.

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Section: Effects On the Pyloric And Gastric Circuitmentioning

confidence: 82%

Sensory-induced modification of two motor patterns in the crab,Cancer pagurus

Smarandache

Stein

2007

Journal of Experimental Biology

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“…Fig . 3C illustrates another, equally important, feature of these PRCs: the effect of increasing the inhibitory synaptic conductance on the oscillator saturates (28,30), so that once the synaptic input has reached a certain level, further increases in its amplitude produce no additional change in the PRC (seen as the overlap in the red and black dots in Fig. 3).…”

Section: Synaptic Strength Does Not Always Mattermentioning

confidence: 97%

“…Nonetheless, there are circumstances in which changes in synaptic strength may have little or no effect on network performance (28,29). This can be seen dramatically if one looks at the effects of inputs to a neuronal oscillator (28,30). It is often useful to determine the influence of an input to an oscillator by measuring its phase-response curve (PRC), which captures the response of the oscillator to perturbations at different times in the oscillator's cycle (31)(32)(33)(34).…”

Section: Synaptic Strength Does Not Always Mattermentioning

confidence: 99%

Variability, compensation, and modulation in neurons and circuits

Marder

2011

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

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334

362

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I summarize recent computational and experimental work that addresses the inherent variability in the synaptic and intrinsic conductances in normal healthy brains and shows that multiple solutions (sets of parameters) can produce similar circuit performance. I then discuss a number of issues raised by this observation, such as which parameter variations arise from compensatory mechanisms and which reflect insensitivity to those particular parameters. I ask whether networks with different sets of underlying parameters can nonetheless respond reliably to neuromodulation and other global perturbations. At the computational level, I describe a paradigm shift in which it is becoming increasingly common to develop families of models that reflect the variance in the biological data that the models are intended to illuminate rather than single, highly tuned models. On the experimental side, I discuss the inherent limitations of overreliance on mean data and suggest that it is important to look for compensations and correlations among as many system parameters as possible, and between each system parameter and circuit performance. This second paradigm shift will require moving away from measurements of each system component in isolation but should reveal important previously undescribed principles in the organization of complex systems such as brains.circuit dynamics | neuronal variability | neuronal homeostasis | dynamic clamp A ll experimental biologists face a daily conundrum: on the one hand, we know that all individual biological organisms, be they lobsters, cats, or humans, are distinct individuals. On the other hand, we must do experiments on multiple individuals to ensure the reliability of our results. As biologists wishing to understand how the function of a cell, a circuit, or a brain depends on the properties of its constituent processes, we confront two issues: (i) all our data come with associated measurement error (often difficult to assess), and (ii) there is considerable natural variability in the populations we study. Consequently, we conventionally rely on statistics calculated from populations to assure ourselves that our measurements are reliable. Most commonly, we report mean data, with the underlying assumption that these means capture something akin to a "platonic ideal" of the individual neurons or animals whose properties were measured. Although this strategy has been enormously useful over the years, it has many limitations, some of which I discuss below. Multiple Solutions and Failure of AveragingDespite the widespread use of means, computational work has shown unambiguously some of the dangers and confounds that can come from exclusive reliance on mean data (1-3). One example of this comes from a study in which several thousand model neurons were generated by randomly picking the maximal conductances of the five different ionic conductances in the model (2). Fig. 1 shows three examples of single-spike bursters chosen from a population of 164 single-spike bursters generated in this study. Alth...

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“…However, the application of 4-AP and ATX II also changed spike shape, which will directly affect neuronal firing properties and synaptic transmission. Thus, the overall effects of changing I A or I Na ϩ properties on network activities would be multifaceted and intricate to interpret (Prinz et al, 2003). The contribution of both currents to activity gating can be explained by interpolating firing thresholds at different membrane potentials (Fig.…”

Section: Discussionmentioning

confidence: 99%

Selective Gating of Neuronal Activity by Intrinsic Properties in Distinct Motor Rhythms

2015

Journal of Neuroscience

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Many neural circuits show fast reconfiguration following altered sensory or modulatory inputs to generate stereotyped outputs. In the motor circuit of Xenopus tadpoles, I study how certain voltage-dependent ionic currents affect firing thresholds and contribute to circuit reconfiguration to generate two distinct motor patterns, swimming and struggling. Firing thresholds of excitatory interneurons [i.e., descending interneurons (dINs)] in the swimming central pattern generator are raised by depolarization due to the inactivation of Na ϩ currents. In contrast, the thresholds of other types of neurons active in swimming or struggling are raised by hyperpolarization from the activation of fast transient K ϩ currents. The firing thresholds are then compared with the excitatory synaptic drives, which are revealed by blocking action potentials intracellularly using QX314 during swimming and struggling. During swimming, transient K ϩ currents lower neuronal excitability and gate out neurons with weak excitation, whereas their inactivation by strong excitation in other neurons increases excitability and enables fast synaptic potentials to drive reliable firing. During struggling, continuous sensory inputs lead to high levels of network excitation. This allows the inactivation of Na ϩ currents and suppression of dIN activity while inactivating transient K ϩ currents, recruiting neurons that are not active in swimming. Therefore, differential expression of these currents between neuron types can explain why synaptic strength does not predict firing reliability/intensity during swimming and struggling. These data show that intrinsic properties can override fast synaptic potentials, mediate circuit reconfiguration, and contribute to motor-pattern switching.

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The Functional Consequences of Changes in the Strength and Duration of Synaptic Inputs to Oscillatory Neurons

Cited by 118 publications

References 47 publications

Sensory-induced modification of two motor patterns in the crab,Cancer pagurus

Sensory-induced modification of two motor patterns in the crab,Cancer pagurus

Variability, compensation, and modulation in neurons and circuits

Selective Gating of Neuronal Activity by Intrinsic Properties in Distinct Motor Rhythms

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