Coath M, Balaguer-Ballester E, Denham S L, Denham M
Centre for Theoretical and Computational Neuroscience, University of Plymouth, Plymouth, UK.
Biosystems. 2008 Oct-Nov;94(1-2):60-7. doi: 10.1016/j.biosystems.2008.05.011. Epub 2008 Jun 20.
The responses of cortical neurons are often characterized by measuring their spectro-temporal receptive fields (STRFs). The STRF of a cell can be thought of as a representation of its stimulus 'preference' but it is also a filter or 'kernel' that represents the best linear prediction of the response of that cell to any stimulus. A range of in vivo STRFs with varying properties have been reported in various species, although none in humans. Using a computational model it has been shown that responses of ensembles of artificial STRFs, derived from limited sets of formative stimuli, preserve information about utterance class and prosody as well as the identity and sex of the speaker in a model speech classification system. In this work we help to put this idea on a biologically plausible footing by developing a simple model thalamo-cortical system built of conductance based neurons and synapses some of which exhibit spike-time-dependent plasticity. We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration. These model neurons also, in common with neurons measured in vivo, exhibit a wide range of non-linearities; this deviation from linearity can be exposed by characterizing the difference between the measured response of each neuron to a stimulus, and the response predicted by the STRF estimated for that neuron. The proposed model, with its simple architecture, learning rule, and modest number of neurons (<1000), is suitable for implementation in neuromorphic analogue VLSI hardware and hence could form the basis of a developmental, real time, neuromorphic sound classification system.
皮层神经元的反应通常通过测量其光谱-时间感受野（STRF）来表征。一个细胞的STRF可以被认为是其刺激“偏好”的一种表示，但它也是一个滤波器或“核”，代表该细胞对任何刺激反应的最佳线性预测。尽管在人类中尚未有报道，但在各种物种中已经报道了一系列具有不同特性的体内STRF。使用计算模型已经表明，从有限的形成性刺激集合中导出的人工STRF集合的反应，在模型语音分类系统中保留了关于话语类别和韵律以及说话者身份和性别的信息。在这项工作中，我们通过开发一个由基于电导的神经元和突触构建的简单丘脑-皮层系统模型，帮助将这一想法置于生物学上合理的基础上，其中一些神经元和突触表现出脉冲时间依赖性可塑性。我们表明，在这样一个模型中的神经元在暴露于形成性刺激时会发展出具有不同时间特性的STRF，表现出一系列异位整合。这些模型神经元也与体内测量的神经元一样，表现出广泛的非线性；这种与线性的偏差可以通过表征每个神经元对刺激的测量反应与为该神经元估计的STRF预测的反应之间的差异来揭示。所提出的模型具有简单的架构、学习规则和适度数量的神经元（<1000），适合在神经形态模拟VLSI硬件中实现，因此可以构成一个发育性、实时、神经形态声音分类系统的基础。
