Network dynamics and synchronous activity in cultured cortical neurons

被引:144
作者
Chiappalone, Michela
Vato, Alessandro
Berdondini, Luca
Koudelka-Hep, Milena
Martinoia, Sergio
机构
[1] Univ Genoa, DIBE, Dept Biophys & Elect Engn, Neuroengn & Bionanotechnol Grp, I-16145 Genoa, Italy
[2] Northwestern Univ, Sch Med, Dept Physiol, Chicago, IL 60611 USA
[3] Univ Neuchatel, Inst Microtechnol, Sensors Actuators & Microsyst Lab, CH-2007 Neuchatel, Switzerland
关键词
in-vitro cortical networks; micro-electrode-array; sub-population segregation; electrical and chemical stimulation; burst event; cross-correlation;
D O I
10.1142/S0129065707000968
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Neurons extracted from specific areas of the Central Nervous System (CNS), such as the hippocampus, the cortex and the spinal cord, can be cultured in vitro and coupled with a micro-electrode array (MEA) for months. After a few days, neurons connect each other with functionally active synapses, forming a random network and displaying spontaneous electrophysiological activity. In spite of their simplified level of organization, they represent an useful framework to study general information processing properties and specific basic learning mechanisms in the nervous system. These experimental preparations show patterns of collective rhythmic activity characterized by burst and spike firing. The patterns of electrophysiological activity may change as a consequence of external stimulation (i.e., chemical and/or electrical inputs) and by partly modifying the "randomness" of the network architecture (i.e., confining neuronal sub-populations in clusters with micro-machined barriers). In particular we investigated how the spontaneous rhythmic and synchronous activity can be modulated or drastically changed by focal electrical stimulation, pharmacological manipulation and network segregation. Our results show that burst firing and global synchronization can be enhanced or reduced; and that the degree of synchronous activity in the network can be characterized by simple parameters such as cross-correlation on burst events.
引用
收藏
页码:87 / 103
页数:17
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