Auditory Golgi cells are interconnected predominantly by electrical synapses

Yaeger, Daniel B.; Trussell, Laurence O.

doi:10.1152/jn.01108.2015

Cited by 16 publications

(16 citation statements)

References 73 publications

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“…To achieve further insight into mechanisms underlying the different correlation structures observed in midbrain and forebrain, spike synchrony was computed for all pairs of simultaneously recorded neurons. Spike synchrony has been linked to shared connections between neurons ( Atencio and Schreiner, 2013 ; Miller et al, 2014 ; Atencio et al, 2016 ; Sabri et al, 2016 ; Schwab et al, 2017 ; Yaeger and Trussell, 2016 ). Thus, lack of shared connections between neurons could underlie the low R NC in the forebrain.…”

Section: Resultsmentioning

confidence: 99%

“…Tuning similarity and trial-by-trial variability are known as signal ( Gawne and Richmond, 1993 ) and noise ( van Kan et al, 1985 ) correlation, respectively, and jointly referred to as the correlation structure ( Zohary et al, 1994 ; Bair et al, 2001 ; Seriès et al, 2004 ; Averbeck et al, 2006 ). An additional indicator of shared inputs is spike synchrony ( Miller et al, 2014 ; Atencio et al, 2016 ; Sabri et al, 2016 ; Schwab et al, 2017 ; Yaeger and Trussell, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Distinct Correlation Structure Supporting a Rate-Code for Sound Localization in the Owl’s Auditory Forebrain

Beckert

Pavão

Peña

2017

eNeuro

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While a topographic map of auditory space exists in the vertebrate midbrain, it is absent in the forebrain. Yet, both brain regions are implicated in sound localization. The heterogeneous spatial tuning of adjacent sites in the forebrain compared to the midbrain reflects different underlying circuitries, which is expected to affect the correlation structure, i.e., signal (similarity of tuning) and noise (trial-by-trial variability) correlations. Recent studies have drawn attention to the impact of response correlations on the information readout from a neural population. We thus analyzed the correlation structure in midbrain and forebrain regions of the barn owl’s auditory system. Tetrodes were used to record in the midbrain and two forebrain regions, Field L and the downstream auditory arcopallium (AAr), in anesthetized owls. Nearby neurons in the midbrain showed high signal and noise correlations (RNCs), consistent with shared inputs. As previously reported, Field L was arranged in random clusters of similarly tuned neurons. Interestingly, AAr neurons displayed homogeneous monotonic azimuth tuning, while response variability of nearby neurons was significantly less correlated than the midbrain. Using a decoding approach, we demonstrate that low RNC in AAr restricts the potentially detrimental effect it can have on information, assuming a rate code proposed for mammalian sound localization. This study harnesses the power of correlation structure analysis to investigate the coding of auditory space. Our findings demonstrate distinct correlation structures in the auditory midbrain and forebrain, which would be beneficial for a rate-code framework for sound localization in the nontopographic forebrain representation of auditory space.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distinct Correlation Structure Supporting a Rate-Code for Sound Localization in the Owl’s Auditory Forebrain

Beckert

Pavão

Peña

2017

eNeuro

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“…In particular, the intrinsic excitability (Bal and Oertel, 2001; Ferragamo et al, 1998; Golding et al, 1999; Hirsch and Oertel, 1988a; Hirsch and Oertel, 1988b; Kuo et al, 2012; Manis, 1990; Manis et al, 1994; Oertel, 1983; Oertel et al, 1990; Rodrigues and Oertel, 2006; Street and Manis, 2007; Zhang and Oertel, 1993a; Zhang and Oertel, 1993b), the ion channels that underlie the intrinsic excitability of the cells (Bal and Oertel, 2001; Cao and Oertel, 2011; Cao et al, 2007; Harasztosi et al, 1999; Kanold and Manis, 1999; Kim and Trussell, 2006; Manis and Marx, 1991; Manis et al, 2003; Rothman and Manis, 2003a; Rothman and Manis, 2003c; Rusznák et al, 1996; Rusznák et al, 1997), the kinetics of synaptic conductances (Gardner et al, 1999; Gardner et al, 2001; Harty and Manis, 1998; Kopp-Scheinpflug et al, 2002; Nerlich et al, 2014; Raman and Trussell, 1992; Raman et al, 1994; Xie and Manis, 2013; Xie and Manis, 2014) and the temporal dynamics of synaptic transmitter release (Kuo et al, 2012; Mancilla and Manis, 2009; Roberts and Trussell, 2010; Wang and Manis, 2008; Wang et al, 2010; Xie and Manis, 2013; Xie and Manis, 2017b; Yang and Xu-Friedman, 2008; Yang and Xu-Friedman, 2009) have been quantitatively described for several of the principal cell types. Connectivity amongst the cells within the territories of the cochlear nuclear complex is known qualitatively (Campagnola and Manis, 2014; Mancilla and Manis, 2009; Muniak and Ryugo, 2014; Munirathinam et al, 2004; Oertel et al, 1990; Ostapoff et al, 1999; Roberts and Trussell, 2010; Roberts et al, 2008; Wickesberg and Oertel, 1988; Wickesberg et al, 1991; Yaeger and Trussell, 2015; Yaeger and Trussell, 2016), and new methods are on the horizon to provide better quantitative measures of connections between identified neuronal classes (Deerinck et al, 2015; Denk and Horstmann, 2004; Holcomb et al, 2013;…”

Section: Introductionmentioning

confidence: 99%

A biophysical modelling platform of the cochlear nucleus and other auditory circuits: From channels to networks

Manis

Campagnola

2018

Hearing Research

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Models of the auditory brainstem have been an invaluable tool for testing hypotheses about auditory information processing and for highlighting the most important gaps in the experimental literature. Due to the complexity of the auditory brainstem, and indeed most brain circuits, the dynamic behavior of the system may be difficult to predict without a detailed, biologically realistic computational model. Despite the sensitivity of models to their exact construction and parameters, most prior models of the cochlear nucleus have incorporated only a small subset of the known biological properties. This confounds the interpretation of modelling results and also limits the potential future uses of these models, which require a large effort to develop. To address these issues, we have developed a general purpose, biophysically detailed model of the cochlear nucleus for use both in testing hypotheses about cochlear nucleus function and also as an input to models of downstream auditory nuclei. The model implements conductance-based Hodgkin-Huxley representations of cells using a Python-based interface to the NEURON simulator. Our model incorporates most of the quantitatively characterized intrinsic cell properties, synaptic properties, and connectivity available in the literature, and also aims to reproduce the known response properties of the canonical cochlear nucleus cell types. Although we currently lack the empirical data to completely constrain this model, our intent is for the model to continue to incorporate new experimental results as they become available.

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“…Examples include the fast‐spiking (FS) inhibitory interneurons of the neocortex, the inhibitory Golgi cells of the cerebellum, and the inhibitory Golgi cells of the dorsal cochlear nucleus. And indeed, the electrical PSPs transmitted by cortical FS cells (Galarreta and Hestrin, ; Gibson et al, ) and both types of Golgi cells (Dugué et al, ; Yaeger and Trussell, ) have fast but tiny spikelets followed by relatively large, protracted hyperpolarizations when recorded in vitro . These biphasic electrical PSPs have sequential effects, first briefly (∼2 ms) and modestly exciting and then more slowly (∼50–100 ms) inhibiting.…”

Section: Introductionmentioning

confidence: 99%

Synchrony and so much more: Diverse roles for electrical synapses in neural circuits

Connors

2017

Developmental Neurobiology

114

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Electrical synapses are gap junctions between neurons that are ubiquitous across brain regions and species. The biophysical properties of most electrical synapses are relatively simple—transcellular channels allow nearly ohmic, bidirectional flow of ionic current. Yet these connections can play remarkably diverse roles when placed into different neural circuit contexts. Here I review recent findings illustrating how electrical synapses may excite or inhibit, synchronize or desynchronize, augment or diminish rhythms, phase-shift, detect coincidences, enhance signals relative to noise, adapt, and interact with nonlinear membrane and transmitter-release mechanisms. Most of these functions are likely to be widespread in central nervous systems.

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Auditory Golgi cells are interconnected predominantly by electrical synapses

Abstract: Yaeger DB, Trussell LO. Auditory Golgi cells are interconnected predominantly by electrical synapses.

Cited by 16 publications

References 73 publications

Distinct Correlation Structure Supporting a Rate-Code for Sound Localization in the Owl’s Auditory Forebrain

Distinct Correlation Structure Supporting a Rate-Code for Sound Localization in the Owl’s Auditory Forebrain

A biophysical modelling platform of the cochlear nucleus and other auditory circuits: From channels to networks

Synchrony and so much more: Diverse roles for electrical synapses in neural circuits

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