Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks.
Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks.
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DOI：
10.1038/s41467-017-01109-y
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发表时间：
2017-10-24
影响因子：
16.6
通讯作者：
Silver RA
中科院分区：
文献类型：
--
作者：
Cayco-Gajic NA;Clopath C;Silver RA
模式分离是大脑的基本功能。被认为是这种计算基础的发散前馈网络是广泛存在的，但表现出非常相似的稀疏突触连接。Marr-Albus理论假设，这种网络通过将重叠的活动模式映射到大量稀疏活动的神经元上来分离它们。但是突触输入的空间相关性和网络连接性引入的空间相关性可能会损害性能。为了研究模式分离的结构和功能决定因素，我们建立了具有空间相关输入模式的小脑输入层模型，并系统地改变了它们的突触连接。性能通过在输入或输出模式上训练的分类器的学习速度来量化。我们的研究结果表明，稀疏的突触连接是必不可少的空间相关的输入模式在广泛的网络活动分离，而扩展和相关性，而不是稀疏的活动，是模式分离的主要决定因素。输入去相关、扩展重编码和稀疏活动已被提出来分离前馈网络中的重叠活动模式。在这里，作者使用简化和详细的尖峰模型来阐明突触连接如何影响这些机制的贡献，在小脑皮层模式分离。
Pattern separation is a fundamental function of the brain. The divergent feedforward networks thought to underlie this computation are widespread, yet exhibit remarkably similar sparse synaptic connectivity. Marr-Albus theory postulates that such networks separate overlapping activity patterns by mapping them onto larger numbers of sparsely active neurons. But spatial correlations in synaptic input and those introduced by network connectivity are likely to compromise performance. To investigate the structural and functional determinants of pattern separation we built models of the cerebellar input layer with spatially correlated input patterns, and systematically varied their synaptic connectivity. Performance was quantified by the learning speed of a classifier trained on either the input or output patterns. Our results show that sparse synaptic connectivity is essential for separating spatially correlated input patterns over a wide range of network activity, and that expansion and correlations, rather than sparse activity, are the major determinants of pattern separation. Input decorrelation, expansion recoding and sparse activity have been proposed to separate overlapping activity patterns in feedforward networks. Here the authors use reduced and detailed spiking models to elucidate how synaptic connectivity affects the contribution of these mechanisms to pattern separation in cerebellar cortex.
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DOI：
10.1523/jneurosci.0460-12.2012
发表时间：
2012-08-08
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Duguid I;Branco T;London M;Chadderton P;Häusser M
通讯作者：
Häusser M
影响因子：
16.2
作者：
Brunel, N;Hakim, V;Barbour, B
通讯作者：
Barbour, B
DOI：
10.1073/pnas.0808428106
发表时间：
2009-02-17
影响因子：
11.1
作者：
Bengtsson, Fredrik;Jorntell, Henrik
通讯作者：
Jorntell, Henrik
影响因子：
3.5
作者：
Cannon RC;Gleeson P;Crook S;Ganapathy G;Marin B;Piasini E;Silver RA
通讯作者：
Silver RA
影响因子：
64.8
作者：
de la Rocha, Jaime;Doiron, Brent;Reyes, Alex
通讯作者：
Reyes, Alex