Correlations and Synchrony in Threshold Neuron Models

Tchumatchenko, Tatjana; Malyshev, Aleksey; Geisel, Theo; Volgushev, Maxim; Wolf, Fred

doi:10.1103/physrevlett.104.058102

Cited by 90 publications

(134 citation statements)

References 37 publications

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“…The degree to which spike trains are correlated in the cortex and the mechanisms that determine these correlations are topics of great interest in neural coding (Ecker et al 2010;Renart et al 2010;Cohen and Kohn 2011;de la Rocha et al 2007;Moreno-Bote and Parga 2009;Renart et al 2010;Rosenbaum et al 2010;Tchumatchenko et al 2010;Litwin-Kumar et al 2011;Macke et al 2011;Ly et al 2012;Trousdale et al 2012;Ranganathan and Koester 2011;Shlens et al 2006;Cohen and Maunsell 2009). We used computational modeling and mathematical analysis to show that short-term synaptic depression can drastically reduce correlations between the activity of neurons.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the difficulty of obtaining accurate estimates of spiking correlations in vivo (Ecker et al 2010;Cohen and Kohn 2011) and the obscurity of the sources of correlations measured in complicated networks, computational modeling plays an important role in understanding how correlations arise in networks and how they are affected by various neural mechanisms. Computational studies have been successful at identifying a number of mechanisms that impact the amplitude and structure of correlations in neuronal networks (Binder and Powers 2001;Parga 2006, 2009;de la Rocha et al 2007;Renart et al 2010;Rosenbaum et al 2010;Tchumatchenko et al 2010;Litwin-Kumar et al 2011;Macke et al 2011;Pernice et al 2011;Ly et al 2012;Tetzlaff et al 2012;Trousdale et al 2012), but the impact of short-term synaptic depression on neuronal correlations has not been systematically addressed in the literature.…”

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confidence: 99%

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Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations

Rosenbaum

Rubin

Doiron

2013

Journal of Neurophysiology

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Rosenbaum R, Rubin JE, Doiron B. Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations. J Neurophysiol 109: 475-484, 2013. First published October 31, 2012 doi:10.1152/jn.00733.2012.-Correlated neuronal activity is an important feature in many neural codes, a neural correlate of a variety of cognitive states, as well as a signature of several disease states in the nervous system. The cellular and circuit mechanics of neural correlations is a vibrant area of research. Synapses throughout the cortex exhibit a form of short-term depression where increased presynaptic firing rates deplete neurotransmitter vesicles, which transiently reduces synaptic efficacy. The release and recovery of these vesicles are inherently stochastic, and this stochasticity introduces variability into the conductance elicited by depressing synapses. The impact of spiking and subthreshold membrane dynamics on the transfer of neuronal correlations has been studied intensively, but an investigation of the impact of short-term synaptic depression and stochastic vesicle dynamics on correlation transfer is lacking. We find that short-term synaptic depression and stochastic vesicle dynamics can substantially reduce correlations, shape the timescale over which these correlations occur, and alter the dependence of spiking correlations on firing rate. Our results show that short-term depression and stochastic vesicle dynamics need to be taken into account when modeling correlations in neuronal populations. Synaptic neurotransmitter vesicles are released probabilistically in response to a presynaptic spike, and released vesicles are recovered stochastically over a timescale of several hundred milliseconds (Vere-Jones 1966;Wang 1999;Fuhrmann et al. 2002;Goldman 2004;Rosenbaum et al. 2012). The depletion of neurotransmitter vesicles by trains of presynaptic action potentials gives rise to a form of short-term synaptic depression that is pervasive in the cortex (Zucker and Regehr 2002). We find that when two postsynaptic neurons receive correlated input through depressing synapses, the correlation between the synaptic conductances across the neurons' membranes, as well as the neurons' spike trains, are drastically smaller than the correlations predicted by a nondepressing, static synapse model. This reduction in correlation is especially prevalent at higher presynaptic firing rates that more effectively deplete neurotransmitter vesicles. Coupled with the fact that cellular dynamics suppress correlations at low firing rates (de la Rocha et al. 2007), these results show that a population of neurons with depressing synapses exhibit small correlations over a broad range of firing rates, even when their inputs are strongly correlated. Our conclusions reveal an important, yet often ignored, mechanism that promotes asynchronous spiking activity in densely connected neuronal populations. METHODSWe begin by describing the statistical measures used to quantify correlations in this study. We then describe the presyn...

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations

Rosenbaum

Rubin

Doiron

2013

Journal of Neurophysiology

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“…Correlations between the spike trains of two neurons can be induced by common or correlated inputs to the two neurons, even in the absence of a direct synaptic connection between them (Sears and Stagg, 1976;Binder and Powers, 2001;Powers, 2001, 2002;de la Rocha et al, 2007;Shea-Brown et al, 2008;Tchumatchenko et al, 2008). Assuming that the two neurons, labeled 1 and 2, receive a spike train n pre from N pre common presynaptic neurons on top of other uncorrelated background inputs (Fig.…”

Section: Correlations Arising From Common Inputsmentioning

confidence: 99%

“…Early theoretical studies did not take into account the activity of the surrounding network, and it is only more recently that the effects of background inputs have been assessed using a phenomenological noise model (Herrmann and Gerstner, 2001). For the case of common inputs to the neurons, previous theoretical studies have concentrated on spike-count correlations (de la Rocha et al, 2007;Shea-Brown et al, 2008), and the results for the full CCF appear scarce (Kirkwood and Sears, 1978;Tchumatchenko et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains

Ostojic

Brunel²,

Hakim³

2009

J. Neurosci.

210

253

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Functional interactions between neurons in vivo are often quantified by cross-correlation functions (CCFs) between their spike trains. It is therefore essential to understand quantitatively how CCFs are shaped by different factors, such as connectivity, synaptic parameters, and background activity. Here, we study the CCF between two neurons using analytical calculations and numerical simulations. We quantify the role of synaptic parameters, such as peak conductance, decay time, and reversal potential, and analyze how various patterns of connectivity influence CCF shapes. In particular, we find that the symmetry of the CCF distinguishes in general, but not always, the case of shared inputs between two neurons from the case in which they are directly synaptically connected. We systematically examine the influence of background synaptic inputs from the surrounding network that set the baseline firing statistics of the neurons and modulate their response properties. We find that variations in the background noise modify the amplitude of the cross-correlation function as strongly as variations of synaptic strength. In particular, we show that the postsynaptic neuron spiking regularity has a pronounced influence on CCF amplitude. This suggests an efficient and flexible mechanism for modulating functional interactions.

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“…Although these works have provided new clues about the role of neuronal correlations, there are yet many unsolved questions, such as how neuronal correlations are generated and propagated (Moreno et al, 2002;Moreno-Bote and Parga, 2006;de la Rocha et al, 2007;Ostojic et al, 2009;Renart et al, 2010;Rosenbaum et al, 2010Rosenbaum et al, , 2011Tchumatchenko et al, 2010;Cohen and Kohn, 2011;Tchumatchenko and Wolf, 2011;Helias et al, 2014) and how correlations are shaped by limited information in sensory inputs and by neuronal computations. It is clear that the study of the impact of neuronal correlations on information transmission and brain computation, and vice versa, is still an arena for exciting new discoveries.…”

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confidence: 99%

Correlated neuronal activity and its relationship to coding, dynamics and network architecture

2014

Frontiers Research Topics

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Correlated and synchronous activity in populations of neurons has been observed in many brain regions and has been shown to play a crucial role in cortical coding, attention, and network dynamics (Singer and Gray, 1995;Salinas and Sejnowski, 2001). However, we still lack a detailed knowledge of the origin and function, if any, of neuronal correlations. In this Research Topic, new ideas about these long standing questions are put forward. One group of studies in this Research Topic investigates the interaction of neuronal correlations with cellular and circuit mechanisms at the level of single neurons and cell pairs. Bolhasani et al. (2013) study the interaction between direct synaptic coupling between two neurons with correlated stochastic input to the neurons. They find that excitatory synaptic coupling can alter the transfer of pairwise correlations from current input to spike output. Interestingly, there is an optimal value of synaptic coupling strength for which the sensitivity of output correlations to input correlations is maximized.Bird and Richardson (2014) study the interaction between long term plasticity, synaptic vesicle depletion at multiple release sites and presynaptic spiking correlations. They find that there is an optimal number of release sites for driving postsynaptic spiking when synchrony is present in the presynaptic spike trains. Schwalger and Lindner (2013) investigated correlations between the interspike intervals of oscillator model neurons with adaptation. They reveal a fundamental connection between interval correlations and the phase response curve of the neuron model. They also show that when firing rates are high, negative interval correlations cause long-timescale variability of a model neuron's activity to be small.A second group of studies in this Research Topic investigates neuronal correlations on the level of networks. The key questions that these studies addressing are: (1) How are pairwise and higher order correlations generated in networks and which of them are important for a given network? and (2) How should we uncover and interpret spike train correlations in a given dataset?Four studies Zhou et al. In a complementary study, Barreiro et al. (2014) have focused on the emergence of pairwise and higher order correlations in retina models. The authors find that maximum entropy pairwise models capture surprisingly well the network spiking dynamics. What is surprising about these results is that higher-order correlations in this type of models can be constrained to be far lower than the statistically possible limits and that their strength depends more on the structure of the common input than on the synaptic connectivity profile. Jahnke et al. (2013) focused on spike patterns rather than correlations and proposed a mechanism for precise spike time pattern generation and replay in neural networks that lack strong densely connected feed-forward structures. The authors put forward the hypothesis that a non-linearity in synaptic summation rules may explain the lack of observed ...

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Correlations and Synchrony in Threshold Neuron Models

Cited by 90 publications

References 37 publications

Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations

Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations

How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains

Correlated neuronal activity and its relationship to coding, dynamics and network architecture

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