Activity-Dependent Long-Term Depression of Electrical Synapses

Haas, Julie S.; Zavala, Baltazar; Landisman, Carole E.

doi:10.1126/science.1207502

Cited by 148 publications

(201 citation statements)

References 41 publications

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“…Indeed, the bimodal shape of the cross-correlation function is an important criterion for judging whether two neurons are connected by gap-junction (Brivanlou et al 1998;DeVries 1999;Shlens et al 2008). In a few cases (21 out of 108 neuronal pairs recorded), the cross-correlation may only have a single peak around zero lag due to one-way signal transmission via gap-junction (Haas et al 2011;MannMetzer and Yarom 1999). Note that asymmetric correlations do not necessarily imply asymmetric conductance, and they may occur due to asymmetric input resistances.…”

Section: Correlation Patterns Of Rgcsmentioning

confidence: 99%

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

Xiao

Zhang

Xing

et al. 2013

Journal of Neurophysiology

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Neuronal responses to prolonged stimulation attenuate over time. Here, we ask a fundamental question: is adaptation a simple process for the neural system during which sustained input is ignored, or is it actually part of a strategy for the neural system to adjust its encoding properties dynamically? After simultaneously recording the activities of a group of bullfrog's retinal ganglion cells (dimming detectors) in response to sustained dimming stimulation, we applied a combination of information analysis approaches to explore the time-dependent nature of information encoding during the adaptation. We found that at the early stage of the adaptation, the stimulus information was mainly encoded in firing rates, whereas at the late stage of the adaptation, it was more encoded in neural correlations. Such a transition in encoding properties is not a simple consequence of the attenuation of neuronal firing rates, but rather involves an active change in the neural correlation strengths, suggesting that it is a strategy adopted by the neural system for functional purposes. Our results reveal that in encoding a prolonged stimulation, the neural system may utilize concerted, but less active, firings of neurons to encode information.retinal ganglion cell; luminance adaptation; information coding; neural correlation; discrimination ADAPTATION REFERS TO A GENERAL phenomenon in which a neural system adjusts its response property according to the statistics of external inputs (Kohn 2007;Wark et al. 2007). It has been widely suggested that adaptation underlies the strategy for a neural system to utilize its resource efficiently to encode stimulus information (Fairhall et al. 2001;Gutnisky and Dragoi 2008;Lesica et al. 2007;Maravall et al. 2007). For instance, it was found that the tuning functions of sensory neurons are optimized to match the variance of inputs in a natural environment, so that high encoding accuracy for the whole range of stimuli is achieved (Laughlin 1981). In practice, adaptation can also occur very rapidly in response to sudden changes in the input statistics, so that the stimulus information is transmitted with high fidelity (Fairhall et al. 2001;Sharpee et al. 2006). A recent experimental work reported that in addition to enhancing information representation, adaptation can regulate information content transmitted in the sensory pathway (Wang et al. 2010). Thus, to fully understand how the brain processes stimulus information in a natural dynamical environment, it is critical to unveil the computational roles associated with adaptation.Luminance adaptation, in which a neural system is exposed to a sustained stimulation with constant luminance, is a fundamental visual adaptation process whose biophysical mechanisms and potential computational roles have been intensively studied (Wark et al. 2007). However, the detailed time course as to how the stimulus information is encoded dynamically in the varying neural responses during adaptation remains largely unresolved (Kastner and Baccus 2011). In luminance ad...

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Section: Correlation Patterns Of Rgcsmentioning

confidence: 99%

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

Xiao

Zhang

Xing

et al. 2013

Journal of Neurophysiology

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“…Activity-dependent plasticity of electrical synapses is an increasingly important theme in neuroscience (Haas et al, 2011;Hestrin, 2011;Landisman and Connors, 2005;Pereda et al, 2004;Zsiros and Maccaferri, 2008) and it is important to increase our understanding of how modulating the strength of electrical synapses can optimize the signal processing in the CNS. The gap junction coupling between neurons (and many other cells) is mediated by specialized channel proteins termed connexins (reviewed by Söhl et al, 2005) where post-translational modification by phosphorylation can modulate important functional properties (reviewed by Moreno and Lau, 2007).…”

Section: Introductionmentioning

confidence: 99%

Electrical synapses between AII amacrine cells in the retina: Function and modulation

Hartveit

Veruki

2012

Brain Research

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Adaptation enables the visual system to operate across a large range of background light intensities. There is evidence that one component of this adaptation is mediated by modulation of gap junctions functioning as electrical synapses, thereby tuning and functionally optimizing specific retinal microcircuits and pathways. The AII amacrine cell is an interneuron found in most mammalian retinas and plays a crucial role for processing visual signals in starlight, twilight and daylight. AII amacrine cells are connected to each other by gap junctions, potentially serving as a substrate for signal averaging and noise reduction, and there is evidence that the strength of electrical coupling is modulated by the level of background light. Whereas there is extensive knowledge concerning the retinal microcircuits that involve the AII amacrine cell, it is less clear which signaling pathways and intracellular transduction mechanisms are involved in modulating the junctional conductance between electrically coupled AII amacrine cells. Here we review the current state of knowledge, with a focus on recent evidence that suggests that the modulatory control involves activity-dependent changes in the phosphorylation of the gap junction channels between AII amacrine cells, potentially linked to their intracellular Ca 2+ dynamics.

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“…In this model comprised of neurons connected only through gap junctions, the coupling coefficient between neurons was ϳ0.3, which is somewhat higher than experimental measures of ϳ0.1 (e.g., Haas et al 2011). We then reduced this coefficient to 0.1 (through a 10-fold increase in the mean resistance of the gap junctions) and found that a concomitant increase in the number of postsynaptic targets of each neuron (from 2 to 3) was required in order for sensory inputs to perturb locomotion.…”

Section: Extensive Distribution Of Sensory Inputsmentioning

confidence: 98%

Sensory-evoked perturbations of locomotor activity by sparse sensory input: a computational study

Bui

Brownstone

2015

Journal of Neurophysiology

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Sensory inputs from muscle, cutaneous, and joint afferents project to the spinal cord, where they are able to affect ongoing locomotor activity. Activation of sensory input can initiate or prolong bouts of locomotor activity depending on the identity of the sensory afferent activated and the timing of the activation within the locomotor cycle. However, the mechanisms by which afferent activity modifies locomotor rhythm and the distribution of sensory afferents to the spinal locomotor networks have not been determined. Considering the many sources of sensory inputs to the spinal cord, determining this distribution would provide insights into how sensory inputs are integrated to adjust ongoing locomotor activity. We asked whether a sparsely distributed set of sensory inputs could modify ongoing locomotor activity. To address this question, several computational models of locomotor central pattern generators (CPGs) that were mechanistically diverse and generated locomotor-like rhythmic activity were developed. We show that sensory inputs restricted to a small subset of the network neurons can perturb locomotor activity in the same manner as seen experimentally. Furthermore, we show that an architecture with sparse sensory input improves the capacity to gate sensory information by selectively modulating sensory channels. These data demonstrate that sensory input to rhythm-generating networks need not be extensively distributed.

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Activity-Dependent Long-Term Depression of Electrical Synapses

Cited by 148 publications

References 41 publications

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

Electrical synapses between AII amacrine cells in the retina: Function and modulation

Sensory-evoked perturbations of locomotor activity by sparse sensory input: a computational study

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