2014
DOI: 10.1038/srep04336
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Average synaptic activity and neural networks topology: a global inverse problem

Abstract: The dynamics of neural networks is often characterized by collective behavior and quasi-synchronous events, where a large fraction of neurons fire in short time intervals, separated by uncorrelated firing activity. These global temporal signals are crucial for brain functioning. They strongly depend on the topology of the network and on the fluctuations of the connectivity. We propose a heterogeneous mean–field approach to neural dynamics on random networks, that explicitly preserves the disorder in the topolo… Show more

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Cited by 27 publications
(49 citation statements)
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“…In a series of papers [18,19,24,25] we have analyzed in detail the dynamics of random, uncorrelated, dense networks of excitatory LIF neurons and successfully compared it with the corresponding HMF dynamics. Analogously, in this section, we provide a short summary of some basic dynamical regimes of the HMF dynamics with inhibition presented in Sec.…”
Section: Dynamical Effects Of Inhibitory Neuronsmentioning
confidence: 99%
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“…In a series of papers [18,19,24,25] we have analyzed in detail the dynamics of random, uncorrelated, dense networks of excitatory LIF neurons and successfully compared it with the corresponding HMF dynamics. Analogously, in this section, we provide a short summary of some basic dynamical regimes of the HMF dynamics with inhibition presented in Sec.…”
Section: Dynamical Effects Of Inhibitory Neuronsmentioning
confidence: 99%
“…In [18] and [19] this method was applied to dense uncorrelated networks of excitatory LIF neurons, and it revealed a very good approximation of dynamics (1) for any large finite network. For instance, the HMF approach is effective also for sparse uncorrelated networks, provided they exhibit a sufficiently large average in-degree [19].…”
Section: Heterogeneous Mean Field For a Network Of Excitatory Anmentioning
confidence: 99%
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