Abstract
We use computer simulations to investigate the structural and dynamical properties of a developing neural network whose activity is driven by noise. Structurally, the constructed neural networks in our simulations exhibit the small-world properties that have been observed in several neural networks. The dynamical change of neuronal membrane potential is described by the Hodgkin-Huxley model, and two types of learning rules, including spike-timing-dependent plasticity (STDP) and inverse STDP, are considered to restructure the synaptic strength between neurons. Clustered synchronized firing (SF) of the network is observed when the network connectivity (number of connections/maximal connections) is about 0.75, in which the firing rate of neurons is only half of the network frequency. At the connectivity of 0.86, all neurons fire synchronously at the network frequency. The network SF frequency increases logarithmically with the culturing time of a growing network and decreases exponentially with the delay time in signal transmission. These conclusions are consistent with experimental observations. The phase diagrams of SF in a developing network are investigated for both learning rules.
| Original language | English |
|---|---|
| Article number | 051923 |
| Journal | Physical Review E - Statistical, Nonlinear, and Soft Matter Physics |
| Volume | 84 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2011 Nov 29 |
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ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability
- Condensed Matter Physics
Cite this
Synchronization in a noise-driven developing neural network. / Lin, I. H.; Wu, R. K.; Chen, C. M.
In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 84, No. 5, 051923, 29.11.2011.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Synchronization in a noise-driven developing neural network
AU - Lin, I. H.
AU - Wu, R. K.
AU - Chen, C. M.
PY - 2011/11/29
Y1 - 2011/11/29
N2 - We use computer simulations to investigate the structural and dynamical properties of a developing neural network whose activity is driven by noise. Structurally, the constructed neural networks in our simulations exhibit the small-world properties that have been observed in several neural networks. The dynamical change of neuronal membrane potential is described by the Hodgkin-Huxley model, and two types of learning rules, including spike-timing-dependent plasticity (STDP) and inverse STDP, are considered to restructure the synaptic strength between neurons. Clustered synchronized firing (SF) of the network is observed when the network connectivity (number of connections/maximal connections) is about 0.75, in which the firing rate of neurons is only half of the network frequency. At the connectivity of 0.86, all neurons fire synchronously at the network frequency. The network SF frequency increases logarithmically with the culturing time of a growing network and decreases exponentially with the delay time in signal transmission. These conclusions are consistent with experimental observations. The phase diagrams of SF in a developing network are investigated for both learning rules.
AB - We use computer simulations to investigate the structural and dynamical properties of a developing neural network whose activity is driven by noise. Structurally, the constructed neural networks in our simulations exhibit the small-world properties that have been observed in several neural networks. The dynamical change of neuronal membrane potential is described by the Hodgkin-Huxley model, and two types of learning rules, including spike-timing-dependent plasticity (STDP) and inverse STDP, are considered to restructure the synaptic strength between neurons. Clustered synchronized firing (SF) of the network is observed when the network connectivity (number of connections/maximal connections) is about 0.75, in which the firing rate of neurons is only half of the network frequency. At the connectivity of 0.86, all neurons fire synchronously at the network frequency. The network SF frequency increases logarithmically with the culturing time of a growing network and decreases exponentially with the delay time in signal transmission. These conclusions are consistent with experimental observations. The phase diagrams of SF in a developing network are investigated for both learning rules.
UR - http://www.scopus.com/inward/record.url?scp=83255175486&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83255175486&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.84.051923
DO - 10.1103/PhysRevE.84.051923
M3 - Article
C2 - 22181460
AN - SCOPUS:83255175486
VL - 84
JO - Physical Review E
JF - Physical Review E
SN - 2470-0045
IS - 5
M1 - 051923
ER -