Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity

被引:359
作者
Börgers, C
Kopell, N
机构
[1] Tufts Univ, Dept Math, Medford, MA 02155 USA
[2] Boston Univ, Dept Math, Boston, MA 02215 USA
[3] Boston Univ, Ctr Biodynam, Boston, MA 02215 USA
关键词
D O I
10.1162/089976603321192059
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In model networks of E-cells and I-cells (excitatory and inhibitory neurons, respectively), synchronous rhythmic spiking often comes about from the interplay between the two cell groups: the E-cells synchronize the I-cells and vice versa. Under ideal conditions-homogeneity in relevant network parameters and all-to-all connectivity, for instance-this mechanism can yield perfect synchronization. We find that approximate, imperfect synchronization is possible even with very sparse, random connectivity. The crucial quantity is the expected number of inputs per cell. As long as it is large enough (more precisely, as long as the variance of the total number of synaptic inputs per cell is small enough), tight synchronization is possible. The desynchronizing effect of random connectivity can be reduced by strengthening the E-->I synapses. More surprising, it cannot be reduced by strengthening the I-->E synapses. However, the decay time constant of inhibition plays an important role. Faster decay yields tighter synchrony. In particular, in models in which the inhibitory synapses are assumed to be instantaneous, the effects of sparse, random connectivity cannot be seen.
引用
收藏
页码:509 / 538
页数:30
相关论文
共 31 条
[1]   PROPERTIES OF SPARSELY CONNECTED EXCITATORY NEURAL NETWORKS [J].
BARKAI, E ;
KANTER, I ;
SOMPOLINSKY, H .
PHYSICAL REVIEW A, 1990, 41 (02) :590-597
[2]  
Braitenberg V., 1998, CORTEX STAT GEOMETRY, DOI [DOI 10.1007/978-3-662-03733-1_27, 10.1007/978-3-662-03733-1]
[3]   Fast global oscillations in networks of integrate-and-fire neurons with low firing rates [J].
Brunel, N ;
Hakim, V .
NEURAL COMPUTATION, 1999, 11 (07) :1621-1671
[4]   Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons [J].
Brunel, N .
JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 2000, 8 (03) :183-208
[5]   Synchronization of the neural response to noisy periodic synaptic input [J].
Burkitt, AN ;
Clark, GM .
NEURAL COMPUTATION, 2001, 13 (12) :2639-2672
[6]   Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models [J].
Bush, P ;
Sejnowski, T .
JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 1996, 3 (02) :91-110
[7]  
DIENER F, 1985, CR HEBD ACAD SCI, V302, P55
[8]  
DIENER M, 1985, CR ACAD SCI I-MATH, V301, P899
[9]   Stable propagation of synchronous spiking in cortical neural networks [J].
Diesmann, M ;
Gewaltig, MO ;
Aertsen, A .
NATURE, 1999, 402 (6761) :529-533
[10]   Type I membranes, phase resetting curves, and synchrony [J].
Ermentrout, B .
NEURAL COMPUTATION, 1996, 8 (05) :979-1001