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神经元放电率自稳态是指大脑神经网络的放电率维持在相对稳定的状态. 大量实验研究发现神经元放电率自稳态是神经电活动的重要特征, 并且放电率自稳态是实现神经信息处理及维持正常脑功能的基础, 因此放电率自稳态的研究是神经科学领域的重要科学问题. 脑神经网络是一个高度复杂的动态系统, 存在大量输入扰动信号及由于动态链接导致的参数摄动, 因此如何建立并维持神经元放电率自稳态及其鲁棒性仍有待深入研究. 反馈神经回路是皮层神经网络的典型连接模式, 抑制性突触可塑性对神经元放电率自稳态具有重要的调控作用. 本文通过构建包含抑制性突触可塑性的反馈神经回路模型对神经元放电率自稳态及其鲁棒性进行计算研究. 结果表明: 在抑制性突触可塑性的作用下, 神经元放电率可自适应地跟踪目标放电率, 从而取得放电率自稳态; 在有外部输入干扰和参数摄动的情况下, 神经元放电率具有良好的抗扰动性能, 表明放电率自稳态具有很强的鲁棒性; 理论分析揭示了抑制性突触可塑性学习规则是神经元放电率自稳态的神经机制; 仿真分析进一步揭示了学习率及目标放电率对放电率自稳态建立过程具有重要影响.Neural firing rate homeostasis, as an important feature of neural electrical activity, means that the firing rate in brain is maintained in a relatively stable state, and fluctuates around a constant value. Extensive experimental studies have revealed that the firing rate homeostasis is ubiquitous in brain, and provides a base for neural information processing and maintaining normal neurological functions, so that the research on neural firing rate homeostasis is a central problem in the field of neuroscience. Cortical neural network is a highly complex dynamic system with a large number of input disturbance signals and parameter perturbations due to dynamic connection. However, it remains to be further investigated how firing rate homeostasis is established in cortical neural network, furthermore, maintains robustness to these disturbances and perturbations. The feedback neural circuit with recurrent excitatory and inhibitory connection is a typical connective pattern in cortical cortex, and inhibitory synaptic plasticity plays a crucial role in achieving neural firing rate homeostasis. Here, by constructing a feedback neural network with inhibitory spike timing-dependent plasticity (STDP), we conduct a computational research to elucidate the mechanism of neural firing rate homeostasis. The results indicate that the neuronal firing rate can track the target firing rate accurately under the regulation of inhibitory synaptic plasticity, thus achieve firing rate homeostasis. In the face of external disturbances and parameter perturbations, the neuron firing rate deviates transiently from the target firing rate value, and converges to the target firing rate value at a steady state, which demonstrates that the firing rate homeostasis established by the inhibitory synaptic plasticity can maintain strong robustness. Furthermore, the analytical research qualitatively explains the firing rate homeostasis mechanism underlined by inhibitory synaptic plasticity. Finally, the simulations further demonstrate that the learning rate value and the firing rate set point value also exert a quantitative influence on the firing rate homeostasis. Overall, these findings not only gain an insight into the firing rate homeostasis mechanism underlined by inhibitory synaptic plasticity, but also inspire testable hypotheses for future experimental studies.
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Keywords:
- inhibitory synaptic plasticity /
- firing rate homeostasis /
- robustness
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[32] Chacron M J, André L, Leonard M 2005 Phys. Rev. E 72 051917Google Scholar
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[35] 王美丽, 王俊松 2015 64 108701Google Scholar
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[36] Vogels T P, Abbott L F 2009 Nat. Neurosci. 12 483Google Scholar
[37] Stepp N, Plenz D, Srinivasa N 2015 Plos Comput. Biol. 11 e1004043Google Scholar
[38] Vogels T P, Sprekeler H, Zenke F, Clopath C, Gerstner W 2011 Science 334 1569Google Scholar
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图 10 参数干扰时有无抑制性突触可塑性两种情况下的神经元平均放电率自稳态特性 (a)参数摄动信号; (b)神经元膜电位; (c)神经元平均放电率曲线; (d)抑制性突触权重变化曲线
Fig. 10. The firing rate characteristics with and without inhibitory synaptic plasticity under parameter perturbation: (a) The parameter perturbation signal; (b) neural membrane potential; (c) the average firing rate; (d) the strength of inhibitory synapse.
表 1 神经网络结构相关参数取值
Table 1. Parameters values of the neural feedback model structure.
参数 描述 取值 NE 兴奋性神经元规模 800 NI 抑制性神经元规模 200 PEE E-E的连接概率 0.2 PEI E-I的连接概率 0.4 PIE I-E的连接概率 0.4 PII I-I的连接概率 0.4 表 2 神经元模型各参数取值
Table 2. Parameters values of LIF neuron model.
参数 取值 单位 ${\tau _{\rm{m}}}$ 20 ms ${V^{{\rm{rest}}}}$ –60 mV ${V_{{\rm{th}}}}$ –50 mV ${V^{\rm{E}}}$ 0 mV ${V^{\rm{I}}}$ –70 mV ${g^{{\rm{leak}}}}$ 10 nS 表 3 突触模型参数取值
Table 3. Parameters values of synapse model.
参数 取值 单位 ${\tau _{\rm{E}}}$ 5 ms ${\tau _{\rm{I}}}$ 10 ms ${\bar g^{\rm{E}}}$ 140 pS ${\bar g^{\rm{I}}}\normalsize$ 350 pS 表 4 抑制性突触可塑性的参数取值
Table 4. Parameters values of inhibitory synaptic plasticity.
参数 描述 取值 ${W_{ij}}^{}$ 抑制性突触权重 0 ${\tau _{{\rm{STDP}}}}$ 可塑性时间常数 20 $\eta $ 学习率 0.005 $\alpha $ 抑制因子 0.12 ${\rho _0}\normalsize$ 目标放电率 -
[1] Gläser C, Joublin F 2011 IEEE T. Auton. Ment. De. 3 285Google Scholar
[2] Hengen K B, Lambo M E, Hooser S D, van Katz D B, Turrigiano G G 2013 Neuron 80 335Google Scholar
[3] Corner M A, Ramakers G J A 1992 Dev. Brain Res. 65 57Google Scholar
[4] Ramakers G J A, Corner M A, Habets A M M C 1990 Exp. Brain Res. 79 157Google Scholar
[5] Ramakers G J A, Galen H V, Feenstra M G P, Corner M A, Boer G J 1994 Int. J. Dev. Neurosci. 12 611Google Scholar
[6] Pol A N V D, Obrietan K, Belousov A 1996 Neuroscience 74 653Google Scholar
[7] Turrigiano G G, Leslie K R, Desai N S, Rutherford L C, Nelson S B 1998 Nature 391 892Google Scholar
[8] Rutherford L C, Nelson S B, Turrigiano G G 1998 Neuron 21 521Google Scholar
[9] Burrone J, O'Byrne M, Murthy V N 2002 Nature 420 414Google Scholar
[10] Turrigiano G G, Nelson S B 2004 Nat. Rev. Neurosci. 5 97Google Scholar
[11] Turrigiano G 2012 CSH Perspect. Biol. 4 a005736Google Scholar
[12] Cannon J, Miller P 2016 J. Neurophysiol. 116 2004Google Scholar
[13] Cannon J, Miller P 2017 J. Math. Neurosc. 7 1Google Scholar
[14] Miller P, Cannon J 2018 Biol. Cybern. 113 47
[15] McClelland J L, McNaughton B L, O'Reilly R C 1995 Psychol. Rev. 102 419Google Scholar
[16] Frankland P W, O'Brien C, Ohno M, Kirkwood A, Silva A J 2001 Nature 411 309Google Scholar
[17] Carcea I, Froemke R C 2013 Prog. Brain. Res. 207 65Google Scholar
[18] Martin S J, Grimwood P D, Morris R G M 2000 Annu. Rev. Neurosci. 23 649Google Scholar
[19] Sanderson J L, Dell'Acqua M L 2011 Neuroscientist 17 321Google Scholar
[20] Yong L, Kauer J A 2010 Synapse 51 1Google Scholar
[21] Haas J S, Thomas N, Abarbanel H D I 2006 J. Neurophysiol. 96 3305Google Scholar
[22] D'Amour J A, Froemke R C 2015 Neuron 86 514Google Scholar
[23] Hartmann K, Bruehl C, Golovko T, Draguhn A 2008 Plos One 3 e2979Google Scholar
[24] Tohru K, Kazumasa Y, Yumiko Y, Crair M C, Yukio K 2008 Neuron 57 905Google Scholar
[25] Stephen G, James R W 2001 Cereb. Cortex 11 37Google Scholar
[26] Luz Y, Shamir M 2012 Plos. Comput. Biol. 8 e1002334Google Scholar
[27] Hennequin G, Agnes E J, Vogels T P 2017 Annu. Revi. Neurosci. 40 557Google Scholar
[28] Park H J, Friston K 2013 Science 342 1238411Google Scholar
[29] Isaacson J S, Massimo S 2011 Neuronv 72 231Google Scholar
[30] Maass W, Joshi P, Sontag E D 2007 Plos Comput. Biol. 3 e165Google Scholar
[31] Jansen B H, Rit V G 1995 Biol. Cybern. 73 357Google Scholar
[32] Chacron M J, André L, Leonard M 2005 Phys. Rev. E 72 051917Google Scholar
[33] Froemke R C, Jones B J 2011 Neurosci. Biobehav. R. 35 2105Google Scholar
[34] 王俊松, 徐瑶 2014 63 068701Google Scholar
Wang J S, Xu Y 2014 Acta Phys. Sin. 63 068701Google Scholar
[35] 王美丽, 王俊松 2015 64 108701Google Scholar
Wang M L, Wang J S 2015 Acta Phys. Sin. 64 108701Google Scholar
[36] Vogels T P, Abbott L F 2009 Nat. Neurosci. 12 483Google Scholar
[37] Stepp N, Plenz D, Srinivasa N 2015 Plos Comput. Biol. 11 e1004043Google Scholar
[38] Vogels T P, Sprekeler H, Zenke F, Clopath C, Gerstner W 2011 Science 334 1569Google Scholar
[39] Maass W 2014 P. IEEE 102 860Google Scholar
[40] Mcdonnell M D, Ward L M 2011 Nat. Rev. Neurosci. 12 183Google Scholar
[41] Garrett D D, Mcintosh A R, Grady C L 2011 Nat. Rev. Neurosci. 12 612Google Scholar
[42] Mcdonnell M D, Ward L M 2011 Nat. Rev. Neurosci. 12 415Google Scholar
[43] Turrigiano G G 2008 Cell 135 422Google Scholar
[44] Marder E, Tang L S 2010 Neuron 66 161Google Scholar
[45] Sharon B, Dickman D K, Davis G W 2010 Neuron 66 220Google Scholar
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