Self-sustained asynchronous irregular states and Up-Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons
Self-sustained asynchronous irregular states and Up-Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons
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DOI：
10.1007/s10827-009-0164-4
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发表时间：
2009-12-01
影响因子：
1.2
通讯作者：
Destexhe, Alain
中科院分区：
文献类型：
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作者：
Destexhe, Alain
随机连接的整合和激发（IF）神经元网络显示异步不规则（AI）活动状态，类似于清醒动物大脑皮层中记录的放电活动。然而，目前尚不清楚这种活动状态是否是特定于简单的IF模型，或者它们是否也存在于神经元被赋予类似于电生理测量的复杂内在特性的网络中。在这里，我们研究了AI状态在非线性IF神经元网络中的发生，例如自适应指数IF（Brette-Gerstner-Izhikevich）模型。该模型可以显示低阈值锋电位（LTS）、规则锋电位（RS）或快速锋电位（FS）等内在特性。我们相继调查的振荡和AI动态丘脑，皮质和丘脑皮质网络使用这样的模型。在每种情况下都可以找到人工智能状态，有时网络的大小非常小，只有几十个神经元。我们发现，在皮层或丘脑中存在LTS神经元，解释了相对较小的网络尺寸下AI状态的强劲出现。最后，我们研究了尖峰频率适应（SFA）的作用。在RS细胞具有强SFA的皮质网络中，AI状态是短暂的，但当SFA减少时，AI状态可以自我维持很长一段时间。在丘脑皮层网络中，当皮层本身处于AI状态时，就会发现AI状态，但在强SFA的情况下，丘脑皮层网络会显示向上和向下状态转换，类似于慢波睡眠或麻醉期间的细胞内记录。自我维持的Up和Down状态也可以通过具有LTS细胞的双层皮层网络产生。这些模型表明，内在的属性，如适应和低阈值的爆发活动是至关重要的起源和控制的AI状态在丘脑皮质网络。
Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. However, it is not clear whether such activity states are specific to simple IF models, or if they also exist in networks where neurons are endowed with complex intrinsic properties similar to electrophysiological measurements. Here, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models. AI states can be found in each case, sometimes with surprisingly small network size of the order of a few tens of neurons. We show that the presence of LTS neurons in cortex or in thalamus, explains the robust emergence of AI states for relatively small network sizes. Finally, we investigate the role of spike-frequency adaptation (SFA). In cortical networks with strong SFA in RS cells, the AI state is transient, but when SFA is reduced, AI states can be self-sustained for long times. In thalamocortical networks, AI states are found when the cortex is itself in an AI state, but with strong SFA, the thalamocortical network displays Up and Down state transitions, similar to intracellular recordings during slow-wave sleep or anesthesia. Self-sustained Up and Down states could also be generated by two-layer cortical networks with LTS cells. These models suggest that intrinsic properties such as adaptation and low-threshold bursting activity are crucial for the genesis and control of AI states in thalamocortical networks.