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首页> 外文期刊>Neural computation >Network Amplification of Local Fluctuations Causes High Spike Rate Variability, Fractal Firing Patterns and Oscillatory Local Field Potentials
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Network Amplification of Local Fluctuations Causes High Spike Rate Variability, Fractal Firing Patterns and Oscillatory Local Field Potentials

机译：局部波动的网络放大导致高峰值速率变异性，分形点火模式和振荡局部场电势

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摘要

We investigate a model for neural activity in a two-dimensional sheet of leaky integrate-and-fire neurons with feedback connectivity consisting of local excitation and surround inhibition. Each neuron receives stochastic input from an external source, independent in space and time. As recently suggested by Softky and Koch (1992, 1993), independent stochastic input alone cannot explain the high interspike interval variability exhibited by cortical neurons in behaving monkeys. We show that high variability can be obtained due to the amplification of correlated fluctuations in a recurrent network. Furthermore, the cross-correlation functions have a dual structure, with a sharp peak on top of a much broader hill. This is due to the inhibitory and excitatory feedback connections, which cause “hotspots” of neural activity to form within the network. These localized patterns of excitation appear as clusters or stripes that coalesce, disintegrate, or fluctuate in size while simultaneously moving in a random walk constrained by the interaction with other clusters. The synaptic current impinging upon a single neuron shows large fluctuations at many time scales, leading to a large coefficient of variation (CV) for the interspike interval statistics. The power spectrum associated with single units shows a 1/f decay for small frequencies and is flat at higher frequencies, while the power spectrum of the spiking activity averaged over many cells—equivalent to the local field potential—shows no 1/f decay but a prominent peak around 40 Hz, in agreement with data recorded from cat and monkey cortex (Gray et al. 1990; Eckhorn et al. 1993). Firing rates exhibit self-similarity between 20 and 800 msec, resulting in 1/f-like noise, consistent with the fractal nature of neural spike trains (Teich 1992).
机译：我们调查的二维活动的泄漏集成和火神经元的二维工作表中的神经活动模型与反馈连接包括局部激发和周围抑制。每个神经元都从外部源接收随机输入，其时空独立。正如Softky和Koch（1992，1993）最近所建议的那样，仅靠随机输入本身并不能解释行为猴子中皮层神经元表现出的高穗间间隔变异性。我们显示，由于循环网络中相关波动的放大，可以获得高可变性。此外，互相关函数具有双重结构，在更宽广的山顶上有一个尖峰。这是由于抑制性和兴奋性反馈连接，导致网络中形成神经活动的“热点”。这些局部的激励模式显示为簇或条纹，这些簇或条纹的大小会合并，分解或波动，同时在与其他簇的交互作用所限制的随机游动中移动。冲击单个神经元的突触电流在许多时间尺度上都显示出较大的波动，从而导致尖峰间间隔统计的变异系数（CV）较大。与单个单元相关的功率谱在较小频率下显示1 / f衰减，而在较高频率下平坦，而在许多单元上平均的尖峰活动功率谱（等效于局部场电势）显示没有1 / f衰减，但与猫和猴皮层记录的数据一致，在40 Hz附近有一个突出的峰值（Gray等，1990； Eckhorn等，1993）。发射速率在20到800毫秒之间表现出自相似性，从而产生类似1 / f的噪声，这与神经尖峰序列的分形特性一致（Teich 1992）。

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  • 来源
    《Neural computation》 |1994年第5期|795-836|共42页
  • 作者

    Usher M; Stemmler M; Koch C; Olami Z;

  • 作者单位

    Computation and Neural Systems, 139-74, California Institute of Technology, Pasadena, CA 91125 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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