Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold
Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold
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DOI：
10.3389/neuro.10.009.2009
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发表时间：
2009-07-01
影响因子：
3.2
通讯作者：
Shinomoto, Shigeru
中科院分区：
文献类型：
--
作者：
Kobayashi, Ryota;Tsubo, Yasuhiro;Shinomoto, Shigeru
信息在大脑中通过各种神经元传递，这些神经元对相同的信号有不同的反应。包括大脑认知功能在内的全部特征最终应该通过构建能够精确反映各种神经元尖峰反应的模拟器来理解。神经元建模一直停留在定性水平，最近已经发展到定量水平，但仍然无法准确预测生物数据，需要高计算成本。在这项研究中，我们设计了一个简单、快速的计算模型，可以针对任何皮质神经元进行定制，不仅可以用于复制，还可以用于预测对大幅波动电流的各种锋电位反应。该模型的主要特点是多时间尺度自适应阈值预测器和不可重置的漏积分器。该模型能够再现丰富多样的神经元锋电位响应，包括定期发放，内在爆发，快速发放，和抖动，通过调整只有三个自适应阈值参数。该模型可以在三维参数空间中表达连续变化的射击特性，而不仅仅是那些在传统的离散分类。高灵活性和低计算成本将有助于忠实地模拟真实的大脑功能，并研究网络特性如何受到组成神经元分布特性的影响。
Information is transmitted in the brain through various kinds of neurons that respond differently to the same signal. Full characteristics including cognitive functions of the brain should ultimately be comprehended by building simulators capable of precisely mirroring spike responses of a variety of neurons. Neuronal modeling that had remained on a qualitative level has recently advanced to a quantitative level, but is still incapable of accurately predicting biological data and requires high computational cost. In this study, we devised a simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents. The key features of this model are a multi-timescale adaptive threshold predictor and a nonresetting leaky integrator. This model is capable of reproducing a rich variety of neuronal spike responses, including regular spiking, intrinsic bursting, fast spiking, and chattering, by adjusting only three adaptive threshold parameters. This model can express a continuous variety of the firing characteristics in a three-dimensional parameter space rather than just those identified in the conventional discrete categorization. Both high flexibility and low computational cost would help to model the real brain function faithfully and examine how network properties may be influenced by the distributed characteristics of component neurons.