Functional Responses of Autaptic Neural Circuits to Acoustic Signals

Zhigang Zhu, Xiaofeng Zhang, Yisen Wang, Jun Ma
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Abstract

It is important for functional neurons of animals or human beings to adapt to external stimuli, such as sound, pressure, and light. Regarding this aspect, autaptic neuron enables itself to utilize historical information to modulate its instant dynamics, such that it may be able to behave adaptively. In this paper, a FitzHugh–Nagumo based autaptic neuron is employed to investigate the capability of a sound-sensitive neural circuit’s adaptation and filtering to analog acoustic signals. Extensive simulations are performed for excitatory and inhibitory types of autaptic neurons. The results show that the time-delayed feedback of the excitatory chemical autapse can be tuned to play the role of a narrow-band filter in response to a broadband acoustic signal. While the excitatory chemical autaptic neuron cannot saturate its response amplitude due to its positive feedback gain, the inhibitory chemical autapse can drive the neuron’s amplitude to converge as the intensity of external drive increases, which reveals the capability of adaptation. What’s more, the inhibitory chemical autaptic neuron can also exhibit a novel bursting adaptation, in which the number of spikings contained in one bursting changes as the electrical activity evolves. For electrical autaptic neurons, it is also found that both time-delay feedback gains can effectively modulate the response of neuron to acoustic signal. While the variation of time-lags mainly changes the spiking rates of the excitatory electrical autaptic neuron, the feedback gain alters its response amplitude. Lastly, by carefully tuning the time-lags, the expected subthreshold dynamics for larger inhibitory feedback gains can be switched to nearby quasi-periodic firings, which implies a competing relation between the time-delays and the feedback gains in the spiking dynamics of the inhibitory electrical autaptic neurons. The diverse emerging phenomena are expected to facilitate the design of online or interactive learning artificial neural networks with these functional autaptic neurons.
自适应神经回路对声信号的功能响应
动物或人类的功能神经元适应外界刺激,如声、压、光等,是非常重要的。在这方面,自适应神经元使自己能够利用历史信息来调节其即时动态,从而使其能够自适应行为。本文采用FitzHugh-Nagumo自适应神经元研究声敏感神经回路对模拟声信号的适应和滤波能力。广泛的模拟进行了兴奋和抑制性类型的自适应神经元。结果表明,兴奋性化学反应的时滞反馈可以被调谐为窄带滤波器,以响应宽带声信号。兴奋性化学自闭神经元由于其正反馈增益而不能使其反应幅度饱和,而抑制性化学自闭神经元可以随着外部驱动强度的增加而使神经元的幅度收敛,显示出适应能力。更重要的是,抑制性化学自适应神经元也可以表现出一种新的爆发适应,其中一次爆发中包含的峰值数量随着电活动的演变而变化。对于电自适应神经元,也发现时滞反馈增益都能有效地调节神经元对声信号的响应。时滞的变化主要改变兴奋性电自适应神经元的尖峰速率,反馈增益则改变其响应幅度。最后,通过仔细调整时滞,较大的抑制性反馈增益的预期阈下动态可以切换到附近的准周期放电,这意味着抑制性电自适应神经元的尖峰动态中的时滞和反馈增益之间存在竞争关系。不同的新兴现象有望促进使用这些功能自适应神经元设计在线或交互式学习人工神经网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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