The effects of dynamical synapses on firing rate activity: a spiking neural network model

@article{Khalil2017TheEO,
  title={The effects of dynamical synapses on firing rate activity: a spiking neural network model},
  author={Radwa Khalil and Marie Z. Moftah and Ahmed A. Moustafa},
  journal={European Journal of Neuroscience},
  year={2017},
  volume={46}
}
Accumulating evidence relates the fine‐tuning of synaptic maturation and regulation of neural network activity to several key factors, including GABAA signaling and a lateral spread length between neighboring neurons (i.e., local connectivity). Furthermore, a number of studies consider short‐term synaptic plasticity (STP) as an essential element in the instant modification of synaptic efficacy in the neuronal network and in modulating responses to sustained ranges of external Poisson input… 
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References

SHOWING 1-10 OF 70 REFERENCES
Short-Term Plasticity Optimizes Synaptic Information Transmission
TLDR
An analytical approach to quantify time- and rate-dependent synaptic information transfer during arbitrary spike trains using a realistic model of synaptic dynamics in excitatory hippocampal synapses concludes that STP indeed contributes significantly to synaptic informationTransfer and may serve to maximize information transfer for specific firing patterns of the corresponding neurons.
Stable propagation of synchronous spiking in cortical neural networks
TLDR
The results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.
Short-term synaptic depression and stochastic vesicle dynamics reduce and shape neuronal correlations.
TLDR
It is found that short-term synaptic depression and stochastic vesicle dynamics can substantially reduce correlations, shape the timescale over which these correlations occur, and alter the dependence of spiking correlations on firing rate.
Short-term synaptic plasticity.
TLDR
The evidence for this hypothesis, and the origins of the different kinetic phases of synaptic enhancement, as well as the interpretation of statistical changes in transmitter release and roles played by other factors such as alterations in presynaptic Ca(2+) influx or postsynaptic levels of [Ca(2+)]i are discussed.
Coding of temporal information by activity-dependent synapses.
TLDR
This work quantitatively analyze the information about previous interspike intervals, contained in single responses of dynamic synapses, using methods from information theory applied to experimentally based deterministic and probabilistic phenomenological models of depressing and facilitating synapses.
Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons
TLDR
Sparsely-connected networks of conductance-based integrate-and-fire neurons with balanced excitatory and inhibitory connections with finite axonal propagation speed are studied, finding that first and second-order “mean-field” statistics of such networks do not depend on the details of the connectivity at a microscopic scale.
Neural Networks with Dynamic Synapses
TLDR
A unified phenomenological model is proposed that allows computation of the postsynaptic current generated by both types of synapses when driven by an arbitrary pattern of action potential activity in a presynaptic population and allows for derivation of mean-field equations, which govern the activity of large, interconnected networks.
Persistent Activity in Neural Networks with Dynamic Synapses
TLDR
It is shown that different combinations of synaptic depression and facilitation result in qualitatively different network dynamics with respect to the emergence of the attractor states in interconnected neural networks.
Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons
  • N. Brunel
  • Biology
    Journal of Computational Neuroscience
  • 2004
The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons are studied analytically. The analysis reveals a rich repertoire of states, including synchronous
Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons
  • N. Brunel
  • Psychology
    Journal of Physiology-Paris
  • 2000
...
1
2
3
4
5
...