Spontaneous Slow Oscillations and Sequential Patterns Due to Short-Term Plasticity in a Model of the Cortex
被引:1
|
作者:
Leleu, Timothee
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机构:
Univ Tokyo, Grad Sch Engn, Bunkyo Ku, Tokyo 1138505, JapanUniv Tokyo, Grad Sch Engn, Bunkyo Ku, Tokyo 1138505, Japan
Leleu, Timothee
[1
]
Aihara, Kazuyuki
论文数: 0引用数: 0
h-index: 0
机构:
Univ Tokyo, Grad Sch Engn, Bunkyo Ku, Tokyo 1538505, Japan
Univ Tokyo, Inst Ind Sci, Meguro Ku, Tokyo 1538505, JapanUniv Tokyo, Grad Sch Engn, Bunkyo Ku, Tokyo 1138505, Japan
Aihara, Kazuyuki
[2
,3
]
机构:
[1] Univ Tokyo, Grad Sch Engn, Bunkyo Ku, Tokyo 1138505, Japan
[2] Univ Tokyo, Grad Sch Engn, Bunkyo Ku, Tokyo 1538505, Japan
[3] Univ Tokyo, Inst Ind Sci, Meguro Ku, Tokyo 1538505, Japan
MEMBRANE-POTENTIAL FLUCTUATIONS;
PERSISTENT CORTICAL ACTIVITY;
FAST NETWORK OSCILLATIONS;
PRIMARY VISUAL-CORTEX;
NEURAL-NETWORKS;
IN-VIVO;
NEOCORTICAL NEURONS;
PREFRONTAL CORTEX;
WORKING-MEMORY;
FIRE NEURONS;
D O I:
10.1162/NECO_a_00513
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
We study a realistic model of a cortical column taking into account short-term plasticity between pyramidal cells and interneurons. The simulation of leaky integrate-and-fire neurons shows that low-frequency oscillations emerge spontaneously as a result of intrinsic network properties. These oscillations are composed of prolonged phases of high and low activity reminiscent of cortical up and down states, respectively. We simplify the description of the network activity by using a mean field approximation and reduce the system to two slow variables exhibiting some relaxation oscillations. We identify two types of slow oscillations. When the combination of dynamic synapses between pyramidal cells and those between interneurons accounts for the generation of these slow oscillations, the end of the up phase is characterized by asynchronous fluctuations of the membrane potentials. When the slow oscillations are mainly driven by the dynamic synapses between interneurons, the network exhibits fluctuations of membrane potentials, which are more synchronous at the end than at the beginning of the up phase. Additionally, finite size effect and slow synaptic currents can modify the irregularity and frequency, respectively, of these oscillations. Finally, we consider possible roles of a slow oscillatory input modeling long-range interactions in the brain. Spontaneous slow oscillations of local networks are modulated by the oscillatory input, which induces, notably, synchronization, subharmonic synchronization, and chaotic relaxation oscillations in the mean field approximation. In the case of forced oscillations, the slow population-averaged activity of leaky integrate-and-fire neurons can have both deterministic and stochastic temporal features. We discuss the possibility that long-range connectivity controls the emergence of slow sequential patterns in local populations due to the tendency of a cortical column to oscillate at low frequency.