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To Burst or Not to Burst?

机译:爆还是不爆?

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摘要

It is well known that some neurons tend to fire packets of action potentials followed by periods of quiescence (bursts) while others within the same stage of sensory processing fire in a tonic manner. However, the respective computational advantages of bursting and tonic neurons for encoding time varying signals largely remain a mystery. Weakly electric fish use cutaneous electroreceptors to convey information about sensory stimuli and it has been shown that some electroreceptors exhibit bursting dynamics while others do not. In this study, we compare the neural coding capabilities of tonically firing and bursting electroreceptor model neurons using information theoretic measures. We find that both bursting and tonically firing model neurons efficiently transmit information about the stimulus. However, the decoding mechanisms that must be used for each differ greatly: a non-linear decoder would be required to extract all the available information transmitted by the bursting model neuron whereas a linear one might suffice for the tonically firing model neuron. Further investigations using stimulus reconstruction techniques reveal that, unlike the tonically firing model neuron, the bursting model neuron does not encode the detailed time course of the stimulus. A novel measure of feature detection reveals that the bursting neuron signals certain stimulus features. Finally, we show that feature extraction and stimulus estimation are mutually exclusive computations occurring in bursting and tonically firing model neurons, respectively. Our results therefore suggest that stimulus estimation and feature extraction might be parallel computations in certain sensory systems rather than being sequential as has been previously proposed.
机译:众所周知,一些神经元倾向于发射动作电位的数据包,随后是静止期(爆发),而其他神经元则在感觉处理的同一阶段以补品的方式发射。然而,用于编码时变信号的猝发和强直神经元各自的计算优势在很大程度上仍然是个谜。弱电鱼使用皮肤电感受器来传达有关感觉刺激的信息,并且已经表明,一些电感受器表现出爆发性动力学,而另一些则没有。在这项研究中,我们使用信息理论方法比较了声发射和爆发的电感受器模型神经元的神经编码能力。我们发现,爆发和调性激发模型神经元都有效地传递了有关刺激的信息。但是,每种解码器所必须使用的解码机制差别很大:将需要一个非线性解码器来提取由突发模型神经元传输的所有可用信息,而线性解码器可能足以满足声调发射模型神经元的需要。使用刺激重建技术的进一步研究表明,与声发射模型神经元不同,爆发模型神经元不编码刺激的详细时程。一种新颖的特征检测方法表明,爆发的神经元发出某些刺激特征的信号。最后,我们证明了特征提取和刺激估计是分别在爆发和音调激发模型神经元中发生的互斥计算。因此,我们的结果表明,刺激估计和特征提取在某些感觉系统中可能是并行计算,而不是先前提出的顺序计算。

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