Minimal Size of Cell Assemblies Coordinated by Gamma Oscillations

Börgers, Christoph; Franzesi, Giovanni Talei; LeBeau, Fiona E. N.; Boyden, Edward S.; Kopell, Nancy

doi:10.1371/journal.pcbi.1002362

Cited by 52 publications

(54 citation statements)

References 42 publications

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“…The early peak in high-gamma LFP activity was likely reflective of local motion processing in MT with a bottom-up, sensory-driven, link to behavior. This is consistent with a diversity of previous work showing a link between gamma oscillations and local spiking activity (Borgers et al 2012;Cardin et al 2009;Whittington et al 1997).…”

Section: Discussionsupporting

confidence: 93%

Dynamics of the functional link between area MT LFPs and motion detection

Smith

Beliveau

Schoen

et al. 2015

Journal of Neurophysiology

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-The evolution of a visually guided perceptual decision results from multiple neural processes, and recent work suggests that signals with different neural origins are reflected in separate frequency bands of the cortical local field potential (LFP). Spike activity and LFPs in the middle temporal area (MT) have a functional link with the perception of motion stimuli (referred to as neural-behavioral correlation). To cast light on the different neural origins that underlie this functional link, we compared the temporal dynamics of the neural-behavioral correlations of MT spikes and LFPs. Wide-band activity was simultaneously recorded from two locations of MT from monkeys performing a threshold, two-stimuli, motion pulse detection task. Shortly after the motion pulse occurred, we found that high-gamma (100 -200 Hz) LFPs had a fast, positive correlation with detection performance that was similar to that of the spike response. Beta (10 -30 Hz) LFPs were negatively correlated with detection performance, but their dynamics were much slower, peaked late, and did not depend on stimulus configuration or reaction time. A late change in the correlation of all LFPs across the two recording electrodes suggests that a common input arrived at both MT locations prior to the behavioral response. Our results support a framework in which early high-gamma LFPs likely reflected fast, bottom-up, sensory processing that was causally linked to perception of the motion pulse. In comparison, late-arriving beta and highgamma LFPs likely reflected slower, top-down, sources of neuralbehavioral correlation that originated after the perception of the motion pulse.behavior; cortex; local field potential; MT; vision VISUALLY GUIDED DECISIONS involve multiple neural components, such as sensory and evaluative stages (Gold and Shadlen 2007). But when and how do different neural components make their contribution? This question is typically studied by measuring the trial-by-trial correlation between cortical spiking and perceptual behavior (Parker and Newsome 1998). Interpreting these neural-behavioral correlations, however, is difficult because they do not tell us whether fluctuations in neural activity directly produced fluctuations in perception. Thus additional data are needed to better understand and model the causality behind observed neural-behavioral correlations.Recent efforts have focused on the local field potential (LFP) and suggest that distinct neurophysiological processes can be measured from different LFP frequency bands (Bastos et al. 2015;Katzner et al. 2009;Kopell et al. 2010;Siegel et al. 2012). Gamma-band LFPs are thought to carry stimulus information (Belitski et al. 2010) and reflect the synaptic inputs from earlier stages (Khawaja et al. 2009) and the activity of local neurons (Ray and Maunsell 2011a), while beta-band LFPs may reflect intercortical feedback (Saalmann et al. 2007). Correspondingly, attention increases gamma LFP power in a manner similar to that of local spiking activity, while beta power is attenuated (Khaya...

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Section: Discussionsupporting

confidence: 93%

Dynamics of the functional link between area MT LFPs and motion detection

Smith

Beliveau

Schoen

et al. 2015

Journal of Neurophysiology

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“…The first pool is the readily releasable pool (RRP) and the other is the recycling pool (RP) (15), modeled with mass-action kinetics. Synaptic dynamics has been repeatedly used to describe changes in spike rates in neural network populations (16) and emergence of gamma oscillations (17). Furthermore network connectivity can also participate to define bursting or the oscillation frequency in neural networks (18,19).…”

mentioning

confidence: 99%

Robust network oscillations during mammalian respiratory rhythm generation driven by synaptic dynamics

Guerrier

Hayes

Fortin

et al. 2015

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

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How might synaptic dynamics generate synchronous oscillations in neuronal networks? We address this question in the preBötzinger complex (preBötC), a brainstem neural network that paces robust, yet labile, inspiration in mammals. The preBötC is composed of a few hundred neurons that alternate bursting activity with silent periods, but the mechanism underlying this vital rhythm remains elusive. Using a computational approach to model a randomly connected neuronal network that relies on short-term synaptic facilitation (SF) and depression (SD), we show that synaptic fluctuations can initiate population activities through recurrent excitation. We also show that a two-step SD process allows activity in the network to synchronize (bursts) and generate a population refractory period (silence). The model was validated against an array of experimental conditions, which recapitulate several processes the preBötC may experience. Consistent with the modeling assumptions, we reveal, by electrophysiological recordings, that SF/SD can occur at preBötC synapses on timescales that influence rhythmic population activity. We conclude that nondeterministic neuronal spiking and dynamic synaptic strengths in a randomly connected network are sufficient to give rise to regular respiratory-like rhythmic network activity and lability, which may play an important role in generating the rhythm for breathing and other coordinated motor activities in mammals.central pattern generator | synaptic depression | mathematical modeling | numerical simulations | breathing C entral pattern generators (CPGs) are neuronal circuits that generate coordinated activity in the absence of sensory input (1). One such mammalian CPG, the preBötzinger complex (preBötC), gives rise to the eupneic respiratory rhythm (2, 3). Located in the medulla, the preBötC preserves a spontaneous respiratory-like rhythm when isolated in transverse slices, but the precise nature of the cellular and synaptic mechanisms underlying rhythmogenesis remains elusive (3-7). An early hypothesis was that the neuronal activity is driven by intrinsically bursting pacemaker neurons synchronized via excitatory synaptic connections (2,6,8,9). However, electrophysiological and modeling studies (7, 10-12) now suggest the rhythm emerges through stochastic activation of intrinsic currents conveyed by recurrent synaptic connections, without the need for pacemaker neurons (3,4,11,13,14). In either case, excitatory synapses are required for rhythm generation; the possibility that synaptic properties also underlie periodic burst initiation and termination is yet to be demonstrated.Synaptic transmission relies on the release of vesicles, which can be modulated at the presynaptic terminal. Synaptic depression (SD), based on vesicular release, consists of decaying release probability after sustained activity, which subsequently decreases excitability within the underlying connected network. Conversely, synaptic facilitation (SF) enhances vesicular release probability and promotes neuronal synchronizatio...

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“…Computational studies probing oscillatory synchronous activity in E-I networks, and in particular the intricacies of PING rhythms, are prevalent in the literature (Traub et al 1997;Kopell et al 2010;Whittington et al 2000;Ermentrout and Kopell 1998;Börgers and Kopell 2003;Börgers et al 2012;Börgers and Kopell 2005;Borgers and Walker 2013;Krupa et al 2014;Olufsen et al 2003). These studies pay less attention to the role of intrinsic cellular properties or the impact of more varied network structures, as the conceptual PING mechanism assumes strong E-I and I-E inter-connectivity as well as strong I-I intra-connectivity and burst frequencies are presumed to be dictated by properties of synaptic currents.…”

Section: Discussionmentioning

confidence: 99%

Effects of Neuromodulation on Excitatory–Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

2018

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Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of 123J Nonlinear Sci the PING model. However, when a network's intra-connectivity dominates its interconnectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

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Minimal Size of Cell Assemblies Coordinated by Gamma Oscillations

Cited by 52 publications

References 42 publications

Dynamics of the functional link between area MT LFPs and motion detection

Dynamics of the functional link between area MT LFPs and motion detection

Robust network oscillations during mammalian respiratory rhythm generation driven by synaptic dynamics

Effects of Neuromodulation on Excitatory–Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

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