2016
DOI: 10.1103/physreve.93.012305
|View full text |Cite
|
Sign up to set email alerts
|

Neural networks with excitatory and inhibitory components: Direct and inverse problems by a mean-field approach

Abstract: We study the dynamics of networks with inhibitory and excitatory leak-integrate-and-fire neurons with short-term synaptic plasticity in the presence of depressive and facilitating mechanisms. The dynamics is analyzed by a heterogeneous mean-field approximation, which allows us to keep track of the effects of structural disorder in the network. We describe the complex behavior of different classes of excitatory and inhibitory components, which give rise to a rich dynamical phase diagram as a function of the fra… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
21
0

Year Published

2017
2017
2024
2024

Publication Types

Select...
6

Relationship

3
3

Authors

Journals

citations
Cited by 12 publications
(21 citation statements)
references
References 25 publications
0
21
0
Order By: Relevance
“…When v i reaches the threshold value v th , neuron i emits a spike and the membrane potential v i is reset to the base value v r . Following (di Volo et al 2016), the membrane potential is rescaled by a suitable amount to have the spike threshold set at v th = 1 and the rest potential at v r = 0. The Tsodyks, Uziel, and Markram model (Tsodyks et al 1998;Tsodyks and Markram 1997) is assumed to describe the interactions among neurons, i.e.…”
Section: The Modelmentioning
confidence: 99%
See 3 more Smart Citations
“…When v i reaches the threshold value v th , neuron i emits a spike and the membrane potential v i is reset to the base value v r . Following (di Volo et al 2016), the membrane potential is rescaled by a suitable amount to have the spike threshold set at v th = 1 and the rest potential at v r = 0. The Tsodyks, Uziel, and Markram model (Tsodyks et al 1998;Tsodyks and Markram 1997) is assumed to describe the interactions among neurons, i.e.…”
Section: The Modelmentioning
confidence: 99%
“…The time is rescaled to the membrane time constant τ m = 30ms. The adimensional time constants are consequently set to τ in = 0.2, τ i r = 3.4 if the post-synaptic neuron i is inhibitory, or τ i r = 26.6 if it is excitatory (di Volo et al 2016).…”
Section: The Modelmentioning
confidence: 99%
See 2 more Smart Citations
“…also shown in [28]. Data are obtained from a network of N = 5000 neurons, fI = 0.1, where both PE(k) and PI (k) are Gaussian with standard deviation σ = 10 and average values kE = 100 and kI = 350 respectively.…”
Section: From the Finite Size Model To The Hmf Formulationmentioning
confidence: 99%