Network structure within the cerebellar input layer enables lossless sparse encoding.
Network structure within the cerebellar input layer enables lossless sparse encoding.
复制标题
小脑输入层内的网络结构可实现无损稀疏编码。
DOI：
10.1016/j.neuron.2014.07.020
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发表时间：
2014-08-20
期刊：
影响因子：
16.2
通讯作者：
Silver, R. Angus
中科院分区：
文献类型：
--
作者：
Billings, Guy;Piasini, Eugenio;Lorincz, Andrea;Nusser, Zoltan;Silver, R. Angus
神经网络中的突触连通性被认为决定了它们执行的信息处理，但网络结构-功能关系仍然知之甚少。结合小脑输入层的定量解剖和网络模型的信息论分析，研究了突触连通性对信息传递和加工的影响。简化的二值模型表明，前馈网络中的突触连通性决定了信息传输与稀疏编码之间的取舍。在最广泛的输入活动范围内，每个神经元突触连接较少和网络活动依赖阈值的网络最适合无损稀疏编码。具有实验约束的突触传导和抑制的生物学详细的尖峰网络模型证实了我们的分析预测。我们的研究结果表明，小脑输入层内的突触连通性使有效的无损稀疏编码成为可能。此外，它们还从功能上解释了为什么颗粒细胞大约有四个树突，这一特征自鱼类出现以来就一直在进化上保守。少连接的前馈网络最适合无损、稀疏编码性能最好的网络匹配小脑输入层的突触连接解释为什么小脑颗粒细胞大约有4个树突突触连接被认为决定了神经元网络的信息处理，但网络结构-功能关系尚不清楚。Billings等人表明，前馈网络中的突触连通性决定了信息传输与稀疏编码之间的权衡。
The synaptic connectivity within neuronal networks is thought to determine the information processing they perform, yet network structure-function relationships remain poorly understood. By combining quantitative anatomy of the cerebellar input layer and information theoretic analysis of network models, we investigated how synaptic connectivity affects information transmission and processing. Simplified binary models revealed that the synaptic connectivity within feedforward networks determines the trade-off between information transmission and sparse encoding. Networks with few synaptic connections per neuron and network-activity-dependent threshold were optimal for lossless sparse encoding over the widest range of input activities. Biologically detailed spiking network models with experimentally constrained synaptic conductances and inhibition confirmed our analytical predictions. Our results establish that the synaptic connectivity within the cerebellar input layer enables efficient lossless sparse encoding. Moreover, they provide a functional explanation for why granule cells have approximately four dendrites, a feature that has been evolutionarily conserved since the appearance of fish. Network connectivity sets trade-off between informative and sparse encoding Feedforward networks with few connections are optimal for lossless, sparse encoding Best-performing networks match synaptic connectivity in the cerebellar input layer Explanation for why cerebellar granule cells have approximately 4 dendrites Synaptic connectivity is thought to determine information processing in neuronal networks, yet network structure-function relationships are poorly understood. Billings et al. show that synaptic connectivity within feedforward networks determines the trade-off between information transmission and sparse encoding.
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DOI：
10.1523/jneurosci.5469-07.2008
发表时间：
2008-09-03
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Kanichay RT;Silver RA
通讯作者：
Silver RA
神经结构：在3D空间中建模神经元网络的工具。
影响因子：
16.2
作者：
Gleeson P;Steuber V;Silver RA
通讯作者：
Silver RA
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：
1.9
作者：
FUJITA, M
通讯作者：
FUJITA, M
影响因子：
5.3
作者：
Chiu, CS;Brickley, S;Lester, HA
通讯作者：
Lester, HA