Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity
Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity
复制标题
具有 STDP 学习规则和内在可塑性的尖峰神经网络的计算模型
DOI：
10.1016/j.physa.2017.08.053
复制
发表时间：
2018-02-01
影响因子：
3.3
通讯作者：
Song, Yongduan
中科院分区：
文献类型：
--
作者：
Li, Xiumin;Wang, Wei;Song, Yongduan
近年来，建立脉冲神经网络（SNN）的计算模型，如液态机（LSM），引起了人们越来越多的兴趣。具有神经可塑性的生物启发自组织神经网络可以提高计算性能，其动态记忆和循环连接环的特性使其区别于应用更为广泛的前馈神经网络。尽管已经提出了各种用于类脑学习和信息处理的计算模型，但具有多神经元可塑性的自组织神经网络的建模仍然是一个重要的开放性挑战。主要困难在于不同形式的神经可塑性规则之间的相互作用，以及理解神经网络的结构和动力学如何塑造计算性能。在本文中，我们提出了一种新的方法来开发模型的LSM与生物启发的自组织网络的基础上两个神经可塑性学习规则。兴奋性神经元之间的连接通过尖峰时间依赖性可塑性（STOP）学习来适应;同时，神经元兴奋性的程度通过另一种学习规则：内在可塑性（IP）来调节，以维持适度的平均活动水平。我们的研究表明，LSM与STDP+IP的性能优于LSM与随机SNN或SNN由STDP单独获得。与所提出的方法的显着改善是由于更好地反映了不同的神经元之间的竞争，在开发的SNN模型，以及更有效地编码和处理相关的动态信息，其学习和自组织机制。这一结果为优化具有神经可塑性的脉冲神经网络的计算模型提供了参考。(C)2017爱思唯尔B. V.保留所有权利。
Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STOP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity. (C) 2017 Elsevier B.V. All rights reserved.