基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性动态平衡
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摘要
大脑皮层的兴奋性与抑制性平衡是维持正常脑功能的前提,而其失衡会诱发癫痫、帕金森、抑郁症等多种神经疾病,因此兴奋性与抑制性平衡的研究是脑科学领域的核心科学问题.反馈神经回路是脑皮层网络的典型连接模式,抑制性突触可塑性在兴奋性与抑制性平衡中扮演关键角色.本文首先构建具有抑制性突触可塑性的反馈神经回路模型;然后通过计算模拟研究揭示在抑制性突触可塑性的调控下反馈神经回路的兴奋性与抑制性可取得较高程度的动态平衡,并且二者的平衡对输入扰动具有较强的鲁棒性;其次给出了基于抑制性突触可塑性的反馈神经回路兴奋性与抑制性平衡机理的解释;最后发现反馈回路神经元数目有利于提高兴奋性与抑制性平衡的程度,这在一定程度上解释了为何神经元之间会存在较多的连接.本文的研究对于理解脑皮层的兴奋性与抑制性动态平衡机理具有重要的参考价值.
Cortical cortex is mainly composed of excitatory and inhibitory neurons. Balance between excitation and inhibition is a ubiquitous experimental phenomenon in brain. On the one hand, balanced excitation and inhibition plays a crucial role in maintaining normal brain functions; on the other hand, the loss of balance between the two opposing forces will cause neural diseases, such as epilepsy, Parkinson, schizophrenia, etc. Thus the research on balance between excitation and inhibition increasingly focuses on the field of neuroscience. Feedback neural circuit with recurrent excitatory and inhibitory connections is ubiquitous in cortical cortex. However, it is still little known how to achieve and maintain the balance between excitation and inhibition in feedback neural circuit. In this study it is proposed that inhibitory synaptic plasticity should play a key role in regulating the balance between excitation and inhibition. Firstly, the feedback neural circuit model is constructed using leaky integrate-and-fire neuron model, mainly composed of excitatory feed-forward loop, and excitatory and inhibitory recurrent connections. The proposed inhibitory synaptic model is incorporated into the feedback neural circuit model, and whose mathematical formulation is presented in detail. Secondly, the excitatory and inhibitory synaptic currents are obtained through numerical simulations, which demonstrate that the precise balance between excitation and inhibition is achieved under the regulation of inhibitory synaptic plasticity.Furthermore, the research results show that this balance is robust to the fluctuation inputs and disturbances. Thirdly,the balance mechanism underlined by inhibitory synaptic plasticity is elucidated through theoretical and simulation analysis, separately, which provides a clear explanation and an insight into how to achieve and maintain the balance between excitation and inhibition in a feedback neural circuit. Finally, the numerical results reveal that the neuron numbers in excitatory and inhibitory feedback loop exert an influence on the balance, and the larger number can enhance the balance between excitation and inhibition, which explains, to some extent, why there are dense connections between neurons in brain. The results in this study shed light on the balance mechanism of feedback neural circuit, and provide some clues for understanding the mechanism of balance between excitation and inhibition in the brain area.
Cortical cortex is mainly composed of excitatory and inhibitory neurons. Balance between excitation and inhibition is a ubiquitous experimental phenomenon in brain. On the one hand, balanced excitation and inhibition plays a crucial role in maintaining normal brain functions; on the other hand, the loss of balance between the two opposing forces will cause neural diseases, such as epilepsy, Parkinson, schizophrenia, etc. Thus the research on balance between excitation and inhibition increasingly focuses on the field of neuroscience. Feedback neural circuit with recurrent excitatory and inhibitory connections is ubiquitous in cortical cortex. However, it is still little known how to achieve and maintain the balance between excitation and inhibition in feedback neural circuit. In this study it is proposed that inhibitory synaptic plasticity should play a key role in regulating the balance between excitation and inhibition. Firstly, the feedback neural circuit model is constructed using leaky integrate-and-fire neuron model, mainly composed of excitatory feed-forward loop, and excitatory and inhibitory recurrent connections. The proposed inhibitory synaptic model is incorporated into the feedback neural circuit model, and whose mathematical formulation is presented in detail. Secondly, the excitatory and inhibitory synaptic currents are obtained through numerical simulations, which demonstrate that the precise balance between excitation and inhibition is achieved under the regulation of inhibitory synaptic plasticity.Furthermore, the research results show that this balance is robust to the fluctuation inputs and disturbances. Thirdly,the balance mechanism underlined by inhibitory synaptic plasticity is elucidated through theoretical and simulation analysis, separately, which provides a clear explanation and an insight into how to achieve and maintain the balance between excitation and inhibition in a feedback neural circuit. Finally, the numerical results reveal that the neuron numbers in excitatory and inhibitory feedback loop exert an influence on the balance, and the larger number can enhance the balance between excitation and inhibition, which explains, to some extent, why there are dense connections between neurons in brain. The results in this study shed light on the balance mechanism of feedback neural circuit, and provide some clues for understanding the mechanism of balance between excitation and inhibition in the brain area.
引文
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[4]Lombardi F,Herrmann H J,Perrone-Capano C,Plenz D,de Arcangelis L 2012 Phys.Rev.Lett.108 228703
[5]Vogels T P,Sprekeler H,Zenke F,Clopath C,Gerstner W 2011 Science 334 1569
[6]Atallah B V,Scanziani M 2009 Neuron 62 566
[7]Lim S,Goldman M S 2013 Nat.Neurosci.16 1306
[8]Xia X F,Wang J S 2014 Acta Phys.Sin.63 140503(in Chinese)[夏小飞,王俊松2014物理学报63 140503]
[9]López-Huerta V G,Carrillo-Reid L,Galarraga E,Tapia D,Fiordelisio T,Drucker-Colin R,Bargas J 2013 J.Neurosci.33 4964
[10]van Vreeswijk C,Sompolinsky H 1996 Science 274 1724
[11]Deco G,Corbetta M 2011 The Neuroscientist 17 107
[12]Park H J,Friston K 2013 Science 342 1238411
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[14]Maass W,Joshi P,Sontag E D 2007 PLo S Comput.Biol.3 e165
[15]Shu Y,Hasenstaub A,Mc Cormick D A 2003 Nature 423288
[16]Luz Y,Shamir M 2012 PLo S Comput.Biol.8 e1002334
[17]Turrigiano G G 2008 Cell 135 422
[18]Woodin M A,Ganguly K,Poo M M 2003 Neuron 39807
[19]Jansen B H,Rit V G 1995 Biol.Cybern.73 357
[20]Wang J S,Xu Y 2014 Acta Phys.Sin.63 068701(in Chinese)[王俊松,徐瑶2014物理学报63 068701]
[21]Chacron M J,Longtin A,Maler L 2005 Phys.Rev.E:Stat.Nonlinear Soft Matter Phys.72 051917
[22]Dayan P,Abbott L F 2001 Theoretical Neuroscience(Cambridge,MA:MIT Press)pp195–250