Short-term synaptic dynamics control the activity phase of neurons in an oscillatory network.

Diana Martinez,Haroon Anwar,Amitabha Bose,Farzan Nadim,Dirk M Bucher

doi:10.7554/elife.46911

Diana Martinez, Haroon Anwar + Show 3 more

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https://doi.org/10.7554/elife.46911

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Abstract

In oscillatory systems, neuronal activity phase is often independent of network frequency. Such phase maintenance requires adjustment of synaptic input with network frequency, a relationship that we explored using the crab, Cancer borealis, pyloric network. The burst phase of pyloric neurons is relatively constant despite a > two fold variation in network frequency. We used noise input to characterize how input shape influences burst delay of a pyloric neuron, and then used dynamic clamp to examine how burst phase depends on the period, amplitude, duration, and shape of rhythmic synaptic input. Phase constancy across a range of periods required a proportional increase of synaptic duration with period. However, phase maintenance was also promoted by an increase of amplitude and peak phase of synaptic input with period. Mathematical analysis shows how short-term synaptic plasticity can coordinately change amplitude and peak phase to maximize the range of periods over which phase constancy is achieved.

Highlights

IntroductionOscillatory neural activity is often organized into different phases across groups of neurons, both in brain rhythms associated with cognitive tasks or behavioral states (Hasselmo et al, 2002; Buzsaki and Wang, 2012; Buzsaki and Tingley, 2018), and in central pattern generating (CPG) circuits that drive rhythmic motor behaviors (Marder and Bucher, 2001; Marder et al, 2005; Grillner, 2006; Bucher et al, 2015; Katz, 2016; Stein, 2018)
In many oscillatory systems, for example the pyloric circuit (Hooper, 1997b, a), the relationship between L and P falls between these two extremes
We demonstrated this point in the pyloric follower LP neuron using the following protocol

Summary

Introduction

Oscillatory neural activity is often organized into different phases across groups of neurons, both in brain rhythms associated with cognitive tasks or behavioral states (Hasselmo et al, 2002; Buzsaki and Wang, 2012; Buzsaki and Tingley, 2018), and in central pattern generating (CPG) circuits that drive rhythmic motor behaviors (Marder and Bucher, 2001; Marder et al, 2005; Grillner, 2006; Bucher et al, 2015; Katz, 2016; Stein, 2018). A hallmark of many such patterns is that the relative timing of firing between neurons is well maintained over a range of rhythm frequencies (Dicaprio et al, 1997; Hooper, 1997b, a; Wenning et al, 2004; Marder et al, 2005; Grillner, 2006; Mullins et al, 2011; Le Gal et al, 2017). If the latency of firing across different groups of neurons changes proportionally to the rhythm period, phase (latency over period) is invariant, in some cases providing optimal limb coordination at all speeds (Zhang et al, 2014). Constant phase lags between neighboring segments in the control of swimming in lamprey fish and crayfish can be explained mathematically on the basis of asymmetrically weakly coupled oscillators, but the role of intrinsic and synaptic dynamics within each segment is unknown (Cohen et al, 1992; Skinner and Mulloney, 1998; Grillner, 2006; Mullins et al, 2011; Zhang et al, 2014; Le Gal et al, 2017)

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