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首页> 外文期刊>Biological Cybernetics >Feedback-induced gain control in stochastic spiking networks
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Feedback-induced gain control in stochastic spiking networks

机译:随机尖峰网络中反馈引起的增益控制

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摘要

The joint influence of recurrent feedback and noise on gain control in a network of globally coupled spiking leaky integrate-and-fire neurons is studied theoretically and numerically. The context of our work is the origin of divisive versus subtractive gain control, as mixtures of these effects are seen in a variety of experimental systems. We focus on changes in the slope of the mean firing frequency-versus-input bias (f –I) curve when the gain control signal to the cells comes from the cells’ output spikes. Feedback spikes are modeled as alpha functions that produce an additive current in the current balance equation. For generality, they occur after a fixed minimum delay. We show that purely divisive gain control, i.e. changes in the slope of the f –I curve, arises naturally with this additive negative or positive feedback, due to a linearizing actions of feedback. Negative feedback alone lowers the gain, accounting in particular for gain changes in weakly electric fish upon pharmacological opening of the feedback loop as reported by Bastian (J Neurosci 6:553–562, 1986). When negative feedback is sufficiently strong it further causes oscillatory firing patterns which produce irregularities in the f –I curve. Small positive feedback alone increases the gain, but larger amounts cause abrupt jumps to higher firing frequencies. On the other hand, noise alone in open loop linearizes the f –I curve around threshold, and produces mixtures of divisive and subtractive gain control. With both noise and feedback, the combined gain control schemes produce a primarily divisive gain control shift, indicating the robustness of feedback gain control in stochastic networks. Similar results are found when the “input” parameter is the contrast of a time-varying signal rather than the bias current. Theoretical results are derived relating the slope of the f –I curve to feedback gain and noise strength. Good agreement with simulation results are found for inhibitory and excitatory feedback. Finally, divisive feedback is also found for conductance-based feedback (shunting or excitatory) with and without noise.
机译:从理论和数值上研究了全局反馈尖峰泄漏积分生火神经网络中递归反馈和噪声对增益控制的联合影响。我们的工作背景是除法增益控制与减法增益控制的起源,因为在各种实验系统中都可以看到这些效果的混合。当单元的增益控制信号来自单元的输出尖峰时,我们着眼于平均发射频率与输入偏置(f –I)曲线的斜率变化。反馈尖峰被建模为在电流平衡方程中产生附加电流的alpha函数。通常,它们在固定的最小延迟后发生。我们表明,由于反馈的线性化作用,这种加性的负反馈或正反馈自然会产生纯除数的增益控制,即f –I曲线的斜率变化。仅负反馈会降低增益,尤其是由于Bastian报道,在药理学上打开反馈回路后,弱电鱼的增益会发生变化(J Neurosci 6:553-562,1986)。当负反馈足够强时,会进一步引起振荡点火模式,从而在f –I曲线中产生不规则现象。较小的正反馈仅会增加增益,但是较大的正反馈会突然跳至较高的发射频率。另一方面,开环中的噪声使阈值附近的f –I曲线线性化,并产生了除法和减法增益控制的混合。在噪声和反馈的情况下,组合的增益控制方案产生了主要的分裂增益控制偏移,表明了随机网络中反馈增益控制的鲁棒性。当“输入”参数是随时间变化的信号而不是偏置电流的对比度时,会发现类似的结果。得出理论结果,将f –I曲线的斜率与反馈增益和噪声强度相关联。对于抑制性和兴奋性反馈,发现与模拟结果吻合良好。最后,对于基于传导性的反馈(分流或兴奋性),在有噪声和无噪声的情况下也发现了分裂反馈。

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