Scaling of temporal correlations in densely connected networks of LIF neurons

Manrique, Jesús; Renart, Jaime; Rocha, Jaime de la; Parga, Néstor

doi:10.1186/1471-2202-12-s1-p249

Cited by 1 publication

(4 citation statements)

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“…We have seen that a cortical state characterized by irregular and very weakly correlated firing can emerge in spite of the existence of strong and dense connections [15,51,50]. However population-averaged correlation values larger than those predicted by these networks have been observed.…”

Section: Noise Correlations and Behaviormentioning

confidence: 74%

Section: Correlation Balance Relationships In Lif Networkmentioning

confidence: 85%

“…There are many possible factors which can contribute to correlated variability. The existence of a variety of temporal scales in the cortical network can spoil the tracking mechanism responsible for the cancellation of correlations [51]. Besides, correlations could originate from changes of excitability in the neural population produced by trial-to-trial fluctuating signals.…”

Section: Discussionmentioning

confidence: 99%

“…An analysis of the self-consistency equations ( 48)- (50) shows the existence of balance relationships for correlations in the LIF network [50]. Simulations for the case in which the synaptic characteristic times of excitatory and inhibitory currents are the same confirm that spiking correlations coefficients scale as 1/N [51]. However one can expect that the cancellation of current correlations do not take place under some conditions; for instance, inhibitory tracking of excitatory fluctuations becomes more difficult as the difference between the inhibitory and excitatory synaptic time constants increases.…”

Section: Correlation Balance Relationships In Lif Networkmentioning

confidence: 99%

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Towards a self-consistent description of irregular and asynchronous cortical activity

Parga¹

2013

J. Stat. Mech.

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Experimental evidence shows that cortical activity exhibits correlated variability, often referred to as noise correlations. Reported correlation coefficients cover a wide range of values, from moderate to very small ones. There is an evident need of models and mathematical techniques with which to guide the interpretation of these results. However, the very existence of correlated variability is responsible for the technical difficulties that have prevented theory from making enough progress in determining how noise correlations are related to neuron and network properties. Here we review recent work that we have done to develop a program to study these issues. Given that noise correlations depend on the behavioral state, understanding how they are generated is a critical problem that has to be solved before biophysical models can be used to study behavioral tasks.

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Section: Noise Correlations and Behaviormentioning

confidence: 74%