Elijah Daniel H, Samengo Inés, Montemurro Marcelo A
Faculty of Life Sciences, The University of Manchester Manchester, UK.
Statistical and Interdisciplinary Physics Group, Instituto Balseiro and Centro Atómico Bariloche San Carlos de Bariloche, Argentina.
Front Comput Neurosci. 2015 Sep 23;9:113. doi: 10.3389/fncom.2015.00113. eCollection 2015.
Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.
长期以来,人们一直认为丘脑神经元在感知状态下以紧张性模式放电,而在睡眠和无意识状态下以爆发性模式放电。然而,最近的证据表明,爆发性放电也可能与感觉信息的编码有关。在此,我们探索这种丘脑爆发性放电的神经编码。为了评估爆发性编码是通用的,还是取决于每个爆发性神经元的详细特性,我们分析了两个包含不同生物细节水平的神经元模型。其中一个模型不包含动作电位产生过程中的生物物理过程信息,仅在现象学层面描述神经元活动。第二个模型表示参与动作电位发放和爆发性放电的单个离子电导的变化,需要大量参数。我们使用反向相关方法和信息论分析了模型的输入选择性。我们发现,来自这两个模型的n个动作电位爆发通过响应瞬时输入特征(如斜率、相位、幅度等)的变化来调节其动作电位计数来传输信息。然而,爆发性放电最有效地编码的刺激特征不一定与这些经典特征之一一致。因此,我们在所有可以表示为随时间变化的输入电流的线性变换的特征中寻找最优特征。我们发现,爆发性神经元传输的关于此类更一般特征的信息多6倍。刺激中的相关事件位于爆发开始前约100毫秒和爆发开始后约20毫秒的时间窗口内。最重要的是,简单模型和生物现实模型所采用的神经编码在很大程度上是相同的,这意味着简单的丘脑神经元模型包含了解释丘脑爆发性编码计算特性的基本要素。因此,我们的结果表明,n个动作电位爆发编码是丘脑神经元的一个普遍特性。