The spread of rate and correlation in stationary cortical networks

Tetzlaff, Tom; Buschermöhle, Michael; Geisel, Theo; Diesmann, Markus

doi:10.1016/s0925-2312(02)00854-8

Cited by 33 publications

(49 citation statements)

References 6 publications

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“…This is in line with several other theoretical studies on the correlation transmission by pairs of neurons (Shadlen & Newsome, 1998;Halliday, 2000;Stroeve & Gielen, 2001;Tetzlaff, Buschermöhle, Geisel, & Diesmann, 2003;Moreno-Bote & Parga, 2006). Moreover, as shown in section 4.2, spike correlations do not affect the input correlations if the network is perfectly balanced (for K E = gK I ; see equation 4.18).…”

Section: Estimation Of Network Connectivity From Measured Correlationssupporting

confidence: 89%

“…The results of sections 4 and 5 and those of several other theoretical studies (Shadlen & Newsome, 1998;Stroeve & Gielen, 2001;Tetzlaff et al, 2003;Moreno-Bote & Parga, 2006;de la Rocha, Doiron, Shea-Brown, Kresimir, & Reyes, 2007;Kriener et al, this issue) suggest that the transmission of small input correlations to output spike correlations is rather weak. Even for networks with connectivities of 0.4 (and therefore input correlations of 0.4), we observe in section 5 average output correlation coefficients on the order of 10 −3 .…”

Section: Correlations In Corticalmentioning

confidence: 65%

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2008

Neural Computation

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Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of spike trains filtered by components of the neural system (synapses, membranes) and the measurement process. It is largely unknown how the * Tom Tetzlaff is presently affiliated with the Norwegian University of Life Sciences. spike train correlation structure is altered by this filtering and what the consequences for the dynamics of the system and for the interpretation of measured correlations are. In this study, we focus on linearly filtered spike trains and particularly consider correlations caused by overlapping presynaptic neuron populations. We demonstrate that correlation functions and statistical second-order measures like the variance, the covariance, and the correlation coefficient generally exhibit a complex dependence on the filter properties and the statistics of the presynaptic spike trains. We point out that both contributions can play a significant role in modulating the interaction strength between neurons or neuron populations. In many applications, the coherence allows a filter-independent quantification of correlated activity. In different network models, we discuss the estimation of network connectivity from the high-frequency coherence of simultaneous intracellular recordings of pairs of neurons. Neural Computation

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Section: Estimation Of Network Connectivity From Measured Correlationssupporting

confidence: 89%

Section: Correlations In Corticalmentioning

confidence: 65%

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2008

Neural Computation

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“…In a previous study (Tetzlaff et al, 2003) we showed that small input correlations are transmitted to the output in approximately linear fashion: c out ≈ γ c c in . Furthermore, it is known that the correlation gain γ c depends on the marginal input statistics (Stroeve & Gielen, 2001;Tetzlaff et al, 2003;Moreno-Bote & Parga, 2006).…”

Section: Output Correlationsmentioning

confidence: 84%

“…It has been demonstrated in earlier studies (Shadlen & Newsome, 1998;Stroeve & Gielen, 2001;Tetzlaff, Buschermöhle, Geisel, & Diesmann, 2003;Moreno-Bote & Parga, 2006) that pairs of integrate-and-fire neurons transmit common input correlations to some extent to their output spike signals. Here, this result is confirmed and extended by comparing correlations c in between input currents with spike count correlations c out (bin size 0.1 ms) in Dale and hybrid networks for different network sizes N (see Figures 7A and 7B, squares).…”

Section: Output Correlationsmentioning

confidence: 97%

“…Furthermore, it is known that the correlation gain γ c depends on the marginal input statistics (Stroeve & Gielen, 2001;Tetzlaff et al, 2003;Moreno-Bote & Parga, 2006). According to section 3.1, the mean and variance of the input do not depend on the network size as long as the number of synapses K E/I is constant and the network is close to balance.…”

Section: Output Correlationsmentioning

confidence: 99%

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Correlations and Population Dynamics in Cortical Networks

et al. 2008

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The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.

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Signal propagation in feedforward neuronal networks with unreliable synapses

Guo

2010

J Comput Neurosci

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In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation.The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks. *

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The spread of rate and correlation in stationary cortical networks

Cited by 33 publications

References 6 publications

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Correlations and Population Dynamics in Cortical Networks

Signal propagation in feedforward neuronal networks with unreliable synapses

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