Spike-frequency adaptation separates transient communication signals from background oscillations
Spike-frequency adaptation separates transient communication signals from background oscillations
复制标题
DOI：
10.1523/jneurosci.4795-04.2005
复制
发表时间：
2005-03-02
影响因子：
5.3
通讯作者：
Maler, L
中科院分区：
文献类型：
--
作者：
Benda, J;Longtin, A;Maler, L
尖峰频率适应是许多神经元的一个显著特征。然而，除了过滤掉刺激强度的缓慢变化外，人们对它在处理与行为相关的自然刺激方面的计算作用知之甚少。在这里，我们提供了一个更复杂的例子，在这个例子中，我们演示了尖峰频率适应如何在分离瞬时信号和较慢振荡信号方面发挥关键作用。我们在体内记录了弱电鱼类Apteronotus Leptorhynchus非常快速适应的电感受器传入。与同性同种异体相互作用引起的较慢振荡(“心跳”)的反应相比，电感受器对快速通信刺激(“小啁啾”)的放电频率反应强烈增强。使用最近提出的尖峰频率适应的通用模型，我们能够准确地预测电感受器对啁啾和心跳的传入反应。模型的参数是根据对阶跃刺激的响应为每个神经元单独确定的。我们的结论是，快速尖峰频率适应的动态足以解释数据。对阶跃反应的额外数据的分析表明，尖峰频率适应在抑制突触的过程中起减法作用，而不是预期的分裂作用。因此，自适应动态是线性的，并创建了截止频率为23赫兹的高通滤波器，将输入中的快速信号与较慢的变化分开。在关于鱼发出啁啾的概率的行为数据中，也可以看到类似的临界频率作为拍频的函数。这些结果表明，尖峰频率自适应通常可以帮助提取不同时间尺度的信号，特别是嵌入在较慢振荡中的高频信号。
Spike- frequency adaptation is a prominent feature of many neurons. However, little is known about its computational role in processing behaviorally relevant natural stimuli beyond filtering out slow changes in stimulus intensity. Here, we present a more complex example in which we demonstrate how spike- frequency adaptation plays a key role in separating transient signals from slower oscillatory signals. We recorded in vivo from very rapidly adapting electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus. The firing- frequency response of electroreceptors to fast communication stimuli (" small chirps") is strongly enhanced compared with the response to slower oscillations (" beats") arising from interactions of same- sex conspecifics. We are able to accurately predict the electroreceptor afferent response to chirps and beats, using a recently proposed general model for spike- frequency adaptation. The parameters of the model are determined for each neuron individually from the responses to step stimuli. We conclude that the dynamics of the rapid spike- frequency adaptation is sufficient to explain the data. Analysis of additional data from step responses demonstrates that spike- frequency adaptation acts subtractively rather than divisively as expected from depressing synapses. Therefore, the adaptation dynamics is linear and creates a high- pass filter with a cutoff frequency of 23 Hz that separates fast signals from slower changes in input. A similar critical frequency is seen in behavioral data on the probability of a fish emitting chirps as a function of beat frequency. These results demonstrate how spike- frequency adaptation in general can facilitate extraction of signals of different time scales, specifically high- frequency signals embedded in slower oscillations.