Inferring input nonlinearities in neural encoding models
Inferring input nonlinearities in neural encoding models
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DOI：
10.1080/09548980701813936
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发表时间：
2008-01-01
影响因子：
7.8
通讯作者：
Sahani, Maneesh
中科院分区：
文献类型：
--
作者：
Ahrens, Misha B.;Paninski, Liam;Sahani, Maneesh
我们描述了一类模型，该模型预测神经元的瞬时放电速率如何依赖于动态刺激。这些模型利用对刺激的逐点非线性变换，然后是作用于变换后的输入序列的线性过滤器。在一种情况下，非线性变换在所有的滤波滞后时间都是相同的。因此，这种“输入非线性”将刺激值的初始数值表示转换为新的表示，从而为后续的线性模型提供最佳输入。我们描述了同时估计输入非线性和线性权重的算法，并提出了正则化和量化估计中的不确定性的技术。在第二种方法中，该模型被推广以允许在每个滞后时间对刺激值进行不同的非线性变换。虽然这个模型更一般，但在算法上更容易适应。然而，它比第一种方法具有更多的自由度，因此需要更多的数据来进行准确的估计。我们在合成数据和啮齿动物桶状皮质神经元的反应上测试了这些方法的可行性。这些模型被证明能够准确地预测对新数据的响应，并恢复几个重要的神经元响应特性。
We describe a class of models that predict how the instantaneous firing rate of a neuron depends on a dynamic stimulus. The models utilize a learnt pointwise nonlinear transform of the stimulus, followed by a linear filter that acts on the sequence of transformed inputs. In one case, the nonlinear transform is the same at all filter lag-times. Thus, this "input nonlinearity" converts the initial numerical representation of stimulus value to a new representation that provides optimal input to the subsequent linear model. We describe algorithms that estimate both the input nonlinearity and the linear weights simultaneously; and present techniques to regularise and quantify uncertainty in the estimates. In a second approach, the model is generalized to allow a different nonlinear transform of the stimulus value at each lag-time. Although more general, this model is algorithmically more straightforward to fit. However, it has many more degrees of freedom than the first approach, thus requiring more data for accurate estimation. We test the feasibility of these methods on synthetic data, and on responses from a neuron in rodent barrel cortex. The models are shown to predict responses to novel data accurately, and to recover several important neuronal response properties.