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Correlations in spiking neuronal networks with distance dependent connections

Birgit Kriener, Moritz Helias, Ad Aertsen, Stefan Rotter
2009 Journal of Computational Neuroscience  
We find that in particular the distribution of correlation coefficients of subthreshold activity can tell apart random networks from networks with distance dependent connectivity.  ...  To approach these questions, we analyze distance dependent correlations of the activity in small-world networks of neurons with current-based synapses derived from a simple ring topology.  ...  These results are all in line with the non-linear dependence of spike train correlations on the strength of input correlations that we observed and fitted by an exponential decay with interneuronal distance  ... 
doi:10.1007/s10827-008-0135-1 pmid:19568923 pmcid:PMC2731936 fatcat:g7anmtuskjdgnjwuoqmepv22p4

The spatial structure of correlated neuronal variability

Robert Rosenbaum, Matthew A Smith, Adam Kohn, Jonathan E Rubin, Brent Doiron
2016 Nature Neuroscience  
In this study, we generalize the theory of correlations in densely connected balanced networks to include the widely observed dependence of synaptic connection probability on distance 34,21 .  ...  However -counter to intuition -balanced networks with dense connectivity show weak spike train correlations 23 .  ...  Next, we generalize these findings to more biologically realistic networks with connection probabilities that depend on neuron distance.  ... 
doi:10.1038/nn.4433 pmid:27798630 pmcid:PMC5191923 fatcat:w3a7bhq2jba5jjgmr4jdx57eim

How Structure Determines Correlations in Neuronal Networks

Volker Pernice, Benjamin Staude, Stefano Cardanobile, Stefan Rotter, Olaf Sporns
2011 PLoS Computational Biology  
This phenomenon is even more pronounced in networks with distance dependent connectivity.  ...  In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations.  ...  Acknowledgments We thank Moritz Helias and Moritz Deger for fruitful discussions and providing an implementation of the Hawkes process in the NEST simulator. Author Contributions  ... 
doi:10.1371/journal.pcbi.1002059 pmid:21625580 pmcid:PMC3098224 fatcat:23zku6qut5c63m5ugin3rbwvr4

Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons

Pierre Yger, Sami El Boustani, Alain Destexhe, Yves Frégnac
2011 Journal of Computational Neuroscience  
We systematically computed the distance-dependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons.  ...  In such balanced networks, we examined the "macroscopic" properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging  ...  Every neuron was sparsely connected with the rest of the network with a connection probability that depended on the distance r ij between two neurons in the network according to a Gaussian profile: p ij  ... 
doi:10.1007/s10827-010-0310-z pmid:21222148 fatcat:x2npahuv6vabfaikuv6jgugkse

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space [article]

Johanna Senk, Espen Hagen, Sacha J. van Albada, Markus Diesmann
2018 arXiv   pre-print
In contrast with the weak spike-train correlations, the correlation of LFP signals is strong and distance-dependent, compatible with experimental observations.  ...  The upscaling preserves the neuron densities, and introduces distance-dependent connection probabilities and delays.  ...  Acknowledgements We would like to thank Hans Ekkehard Plesser for helpful suggestions for the implementation of spatially structured networks in NEST, and Gaute T.  ... 
arXiv:1805.10235v1 fatcat:tcecomg3gvfitgbyq65booatsu

Spatial organization of evoked neuronal dynamics in 2D recurrent networks, with or without structured stimulation

Pierre Yger, Sami El Boustani, Olivier Marre, Andrew P Davison, Alain Destexhe, Yves Frégnac
2009 BMC Neuroscience  
These networks exhibit AI states with cross-correlation functions decreasing exponentially with inter-neuronal distance.  ...  We have simulated sparsely connected networks of conductance-based integrate-and-fire neurons with Gaussian spatial connectivity profiles.  ...  These networks exhibit AI states with cross-correlation functions decreasing exponentially with inter-neuronal distance.  ... 
doi:10.1186/1471-2202-10-s1-p94 fatcat:apy3trj6wreqply6b77o5fjiqy

Effect of network structure on spike train correlations in networks of integrate-and-fire neurons

Volker Pernice, Benjamin Staude, Stefano Cardanobile, Stefan Rotter
2011 BMC Neuroscience  
We study ring networks, where we are able to derive analytical expressions for the distance dependence of correlations and fluctuations in population activity.  ...  Although such networks can assume a state of asynchronous and irregular activity with low firing rates and low pairwise correlations, recurrent connectivity inevitably induces correlations between spike  ...  We study ring networks, where we are able to derive analytical expressions for the distance dependence of correlations and fluctuations in population activity.  ... 
doi:10.1186/1471-2202-12-s1-p272 pmcid:PMC3240381 fatcat:mosapjetdbf45mx4adlvy5bnbq

How network structure shapes pairwise correlations between integrate-and-fire neurons

Birgit Kriener, Marc Timme
2009 BMC Neuroscience  
If we include a distance dependent connectivity ("spatial footprint"), common input correlations become more and more increased the more spatially confined the footprint is, given the mean number of incoming  ...  We can quantitatively describe the distance dependent correlations in a shot-noise approach and even analytically derive the full distribution of input correlation coefficients for certain cases [2].  ...  If we include a distance dependent connectivity ("spatial footprint"), common input correlations become more and more increased the more spatially confined the footprint is, given the mean number of incoming  ... 
doi:10.1186/1471-2202-10-s1-p152 fatcat:m2qvdyp4xre6fgtrteld4rjwl4

The relationship between cortical network structure and the corresponding state space dynamics

Nicole Voges, Laurent Perrinet
2011 BMC Neuroscience  
Neurons are implemented as conductance based integrate-and-fire neurons with distance-dependent synaptic delays. Network dynamics are simulated with NEST/PyNN [6] .  ...  Assuming an enlarged spatial scale we consider distinct network architectures, ranging from purely random or purely locally coupled neurons to distance dependent connectivities that also include patchy  ...  Neurons are implemented as conductance based integrate-and-fire neurons with distance-dependent synaptic delays. Network dynamics are simulated with NEST/PyNN [6] .  ... 
doi:10.1186/1471-2202-12-s1-p345 pmcid:PMC3240462 fatcat:uaumjaqz25ehxlsihnb3n5jd5e

From the statistics of connectivity to the statistics of spike times in neuronal networks

Gabriel Koch Ocker, Yu Hu, Michael A Buice, Brent Doiron, Krešimir Josić, Robert Rosenbaum, Eric Shea-Brown
2017 Current Opinion in Neurobiology  
The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity  ...  of connectivity in determining spike correlations, and shows how the coevolution of structured connectivity and spiking statistics through synaptic plasticity can be predicted self-consistently.  ...  (D) Same as (C) for a motif cumulant expansion (Eq. ( Fig 2 . 2 Correlated activity in balanced networks with spatially dependent connections. (A) Schematic of the two layer network.  ... 
doi:10.1016/j.conb.2017.07.011 pmid:28863386 pmcid:PMC5660675 fatcat:7ebcpbrqrjcufmxx4c3h3qc2lu

From the statistics of connectivity to the statistics of spike times in neuronal networks [article]

Gabriel Koch Ocker, Yu Hu, Michael A. Buice, Brent Doiron, Krešimir Josić, Robert Rosenbaum, Eric Shea-Brown
2017 bioRxiv   pre-print
The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity  ...  The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network.  ...  (D) Same as (C) for a motif cumulant expansion (Eq. ( Fig 2 . 2 Correlated activity in balanced networks with spatially dependent connections. (A) Schematic of the two layer network.  ... 
doi:10.1101/115402 fatcat:zpzmlxjeezc4vj4x55p5ykhqtq

The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding

Robert Meyer, Josef Ladenbauer, Klaus Obermayer
2017 Frontiers in Computational Neuroscience  
We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels.  ...  Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections ("Mexican hat" connectivity) and  ...  Moreover, we want to express our gratitude to Robert Pröpper and Philipp Meier for their help in Python programming.  ... 
doi:10.3389/fncom.2017.00034 pmid:28539881 pmcid:PMC5423970 fatcat:zytokysrbfbaxhxyzfguu26tsq

From the statistics of connectivity to the statistics of spike times in neuronal networks [article]

Gabriel Koch Ocker and Yu Hu, Michael A. Buice, Brent Doiron, Krešimir Josić, Robert Rosenbaum, Eric Shea-Brown
2017 arXiv   pre-print
The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity  ...  The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network.  ...  (D) Same as (C) for a motif cumulant expansion (Eq. ( Fig 2 . 2 Correlated activity in balanced networks with spatially dependent connections. (A) Schematic of the two layer network.  ... 
arXiv:1703.03132v1 fatcat:sorvbqqfdvfyvag474ed3rhkum

Activity Dynamics and Signal Representation in a Striatal Network Model with Distance-Dependent Connectivity

Sebastian Spreizer, Martin Angelhuber, Jyotika Bahuguna, Ad Aertsen, Arvind Kumar
2017 eNeuro  
In contrast, striatal networks with monotonically decreasing distance-dependent connectivity (in a Gaussian fashion) can exhibit only an AI state.  ...  Here, we show that striatal projection neurons should have a nonmonotonically changing distance-dependent connectivity to obtain spatially localized activity patterns in striatum.  ...  Here, we investigate the existence of NAs in a largescale network model of the striatum in which neurons are connected in a distance-dependent manner.  ... 
doi:10.1523/eneuro.0348-16.2017 pmid:28840190 pmcid:PMC5566799 fatcat:tnuyzq253jfadbihv2757f6ufi

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

Sadique Sheik, Martin Coath, Giacomo Indiveri, Susan L. Denham, Thomas Wennekers, Elisabetta Chicca
2012 Frontiers in Neuroscience  
The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity  ...  In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture.  ...  The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity  ... 
doi:10.3389/fnins.2012.00017 pmid:22347163 pmcid:PMC3272652 fatcat:ct6ezvt3ojdxhkjnqo7b2fz7je
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