5 research outputs found

    Two Distinct Mechanisms Mediate Potentiating Effects of Depolarization on Synaptic Transmission

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    Two distinct mechanisms mediate potentiating effects of depolarization on synaptic transmission. Recently there has been renewed interest in a type of plasticity in which a neuron's somatic membrane potential influences synaptic transmission. We study mechanisms that mediate this type of control at a synapse between a mechanoafferent, B21, and B8, a motor neuron that receives chemical synaptic input. Previously we demonstrated that the somatic membrane potential determines spike propagation within B21. Namely, B21 must be centrally depolarized if spikes are to propagate to an output process. We now demonstrate that this will occur with central depolarizations that are only a few millivolts. Depolarizations of this magnitude are not, however, sufficient to induce synaptic transmission to B8. B21-induced postsynaptic potentials (PSPs) are only observed if B21 is centrally depolarized by ≥10 mV. Larger depolarizations have a second impact on B21. They induce graded changes in the baseline intracellular calcium concentration that are virtually essential for the induction of chemical synaptic transmission. During motor programs, subthreshold depolarizations that increase calcium concentrations are observed during one of the two antagonistic phases of rhythmic activity. Chemical synaptic transmission from B21 to B8 is, therefore, likely to occur in a phase-dependent manner. Other neurons that receive mechanoafferent input are electrically coupled to B21. Differential control of spike propagation and chemical synaptic transmission may, therefore, represent a mechanism that permits selective control of afferent transmission to different types of neurons contacted by B21. Afferent transmission to neurons receiving chemical synaptic input will be phase specific, whereas transmission to electrically coupled followers will be phase independent

    Segment specificity of load signal processing depends on walking direction in the stick insect leg muscle control system

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    Akay T, Ludwar BC, Goeritz ML, Schmitz J, Bueschges A. Segment specificity of load signal processing depends on walking direction in the stick insect leg muscle control system. JOURNAL OF NEUROSCIENCE. 2007;27(12):3285-3294

    Motor Outputs in a Multitasking Network: Relative Contributions of Inputs and Experience-Dependent Network States

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    Network outputs elicited by a specific stimulus may differ radically depending on the momentary network state. One class of networks states—experience-dependent states—is known to operate in numerous networks, yet the fundamental question concerning the relative role that inputs and states play in determining the network outputs remains to be investigated in a behaviorally relevant manner. Because previous work indicated that in the isolated nervous system the motor outputs of the Aplysia feeding network are affected by experience-dependent states, we sought to establish the behavioral relevance of these outputs. We analyzed the phasing of firing of radula opening motoneurons (B44 and B48) relative to other previously characterized motoneurons. We found that the overall pattern of motoneuronal firing corresponds to the phasing of movements during feeding behavior, thus indicating a behavioral relevance of network outputs. Previous studies suggested that network inputs act to trigger a response rather than to shape its characteristics, with the latter function being fulfilled by network states. We show this is an oversimplification. In a rested state, different inputs elicited distinct responses, indicating that inputs not only trigger but also shape the responses. However, depending on the combination of inputs and states, responses were either dramatically altered by the network state or were indistinguishable from those observed in the rested state. We suggest that the relative contributions of inputs and states are dynamically regulated and, rather than being fixed, depend on the specifics of states and inputs
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