Behavioral Physiology Group, Department of Biology, Humboldt-Universität zu Berlin, 10115 Berlin, Germany.
J Neurosci. 2012 Jul 18;32(29):10053-62. doi: 10.1523/JNEUROSCI.5911-11.2012.
Sparse coding schemes are employed by many sensory systems and implement efficient coding principles. Yet, the computations yielding sparse representations are often only partly understood. The early auditory system of the grasshopper produces a temporally and population-sparse representation of natural communication signals. To reveal the computations generating such a code, we estimated 1D and 2D linear-nonlinear models. We then used these models to examine the contribution of different model components to response sparseness. 2D models were better able to reproduce the sparseness measured in the system: while 1D models only captured 55% of the population sparseness at the network's output, 2D models accounted for 88% of it. Looking at the model structure, we could identify two types of computation, which increase sparseness. First, a sensitivity to the derivative of the stimulus and, second, the combination of a fast, excitatory and a slow, suppressive feature. Both were implemented in different classes of cells and increased the specificity and diversity of responses. The two types produced more transient responses and thereby amplified temporal sparseness. Additionally, the second type of computation contributed to population sparseness by increasing the diversity of feature selectivity through a wide range of delays between an excitatory and a suppressive feature. Both kinds of computation can be implemented through spike-frequency adaptation or slow inhibition-mechanisms found in many systems. Our results from the auditory system of the grasshopper are thus likely to reflect general principles underlying the emergence of sparse representations.
稀疏编码方案被许多感觉系统采用，实现了有效的编码原则。然而，产生稀疏表示的计算通常只是部分理解。 蝗虫的早期听觉系统对自然通讯信号产生了时空和群体稀疏的表示。为了揭示产生这种编码的计算，我们估计了 1D 和 2D 线性非线性模型。然后，我们使用这些模型来检查不同模型组件对响应稀疏性的贡献。2D 模型能够更好地复制系统中测量到的稀疏性：虽然 1D 模型只能在网络输出处捕获到群体稀疏性的 55%，但 2D 模型则占 88%。从模型结构来看，我们可以识别出两种增加稀疏性的计算类型。首先，对刺激导数的敏感性，其次，快速、兴奋和缓慢、抑制特征的组合。这两种类型都在不同类型的细胞中实现，提高了响应的特异性和多样性。这两种类型产生了更多的瞬态响应，从而放大了时间稀疏性。此外，第二种计算类型通过在兴奋性和抑制性特征之间产生广泛的延迟来增加特征选择性的多样性，从而有助于群体稀疏性。这两种类型的计算都可以通过尖峰频率适应或许多系统中发现的缓慢抑制机制来实现。因此，我们从蝗虫听觉系统中得到的结果可能反映了稀疏表示出现的一般原则。
