Brain state-dependent neuronal computation
Brain state-dependent neuronal computation
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DOI：
10.3389/fncom.2012.00077
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发表时间：
2012-10-01
影响因子：
3.2
通讯作者：
Bernard, Christophe
中科院分区：
文献类型：
--
作者：
Quilichini, Pascale P.;Bernard, Christophe
神经元放电模式包括动作电位的频率和时间，是大脑信息处理的关键组成部分。虽然神经元输出（放电模式）和功能（在一个任务/行为中）之间的关系还没有被完全理解，但现在有相当多的证据表明，一个给定的神经元可以根据大脑状态显示出非常不同的放电模式。因此，这些神经元组装成神经元网络，产生不同的节律（例如，θ波、伽马波和尖波涟漪），这些节律标志着特定的大脑状态（例如，学习、睡眠）。这意味着一个给定的神经网络，由其硬连线的物理连接所定义，可以通过调节其功能连接来支持不同的大脑状态依赖活动。在这里，我们回顾的数据表明，不仅放电模式，而且神经元之间的功能连接，可以动态改变。然后，我们探索这种多功能性的可能机制，重点关注神经元的内在特性和它们建立的突触的特性，以及它们如何被神经调节剂修改，即神经元可以使用从一种通信模式切换到另一种通信模式的不同方式。
Neuronal firing pattern, which includes both the frequency and the timing of action potentials, is a key component of information processing in the brain. Although the relationship between neuronal output (the firing pattern) and function (during a task/behavior) is not fully understood, there is now considerable evidence that a given neuron can show very different firing patterns according to brain state. Thus, such neurons assembled into neuronal networks generate different rhythms (e.g., theta, gamma and sharp wave ripples), which sign specific brain states (e.g., learning, sleep). This implies that a given neuronal network, defined by its hard-wired physical connectivity, can support different brain state-dependent activities through the modulation of its functional connectivity. Here, we review data demonstrating that not only the firing pattern, but also the functional connections between neurons, can change dynamically. We then explore the possible mechanisms of such versatility, focusing on the intrinsic properties of neurons and the properties of the synapses they establish, and how they can be modified by neuromodulators, i.e., the different ways that neurons can use to switch from one mode of communication to the other.