Synaptic Potentiation Facilitates Memory-like Attractor Dynamics in Cultured In Vitro Hippocampal Networks

被引:12
|
作者
Niedringhaus, Mark [1 ]
Chen, Xin [2 ]
Conant, Katherine [1 ,3 ]
Dzakpasu, Rhonda [1 ,2 ,4 ]
机构
[1] Georgetown Univ, Med Ctr, Interdisciplinary Program Neurosci, Washington, DC 20007 USA
[2] Georgetown Univ, Dept Phys, Washington, DC 20007 USA
[3] Georgetown Univ, Med Ctr, Dept Neurosci, Washington, DC 20007 USA
[4] Georgetown Univ, Med Ctr, Dept Physiol & Pharmacol, Washington, DC 20007 USA
来源
PLOS ONE | 2013年 / 8卷 / 03期
基金
美国国家卫生研究院;
关键词
LONG-TERM POTENTIATION; PERSISTENT ACTIVITY; NEURAL-NETWORKS; DENTATE AREA; PLASTICITY; SYNAPSES; MODEL; GAMMA; THETA; INFORMATION;
D O I
10.1371/journal.pone.0057144
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation but how neurons self-organize to produce such activity is not well understood. Attractor-based computational models have been successfully implemented as a theoretical framework for memory storage in networks of neurons. Additionally, activity-dependent modification of synaptic transmission is thought to be the physiological basis of learning and memory. The goal of this study is to demonstrate that using a pharmacological treatment that has been shown to increase synaptic strength within in vitro networks of hippocampal neurons follows the dynamical postulates theorized by attractor models. We use a grid of extracellular electrodes to study changes in network activity after this perturbation and show that there is a persistent increase in overall spiking and bursting activity after treatment. This increase in activity appears to recruit more "errant" spikes into bursts. Phase plots indicate a conserved activity pattern suggesting that a synaptic potentiation perturbation to the attractor leaves it unchanged. Lastly, we construct a computational model to demonstrate that these synaptic perturbations can account for the dynamical changes seen within the network.
引用
收藏
页数:12
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