Deep Learning Models of the Retinal Response to Natural Scenes
Deep Learning Models of the Retinal Response to Natural Scenes
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发表时间：
2017-02
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通讯作者：
Lane T. McIntosh;Niru Maheswaranathan;Aran Nayebi;S. Ganguli;S. Baccus
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作者：
Lane T. McIntosh;Niru Maheswaranathan;Aran Nayebi;S. Ganguli;S. Baccus
感觉神经科学的一个核心挑战是理解与行为学相关的自然刺激编码的神经计算和电路机制。在多层神经回路中，非线性过程，如突触传递和尖峰动力学，对自然刺激响应的精确计算模型的创建提出了重大障碍。在这里，我们证明了深度卷积神经网络（CNN）捕获对自然场景的视网膜反应几乎在细胞反应的可变性范围内，并且比线性-非线性（LN）模型和广义线性模型（GLM）明显更准确。此外，我们还发现了CNN的两个额外的令人惊讶的特性：当在少量数据上训练时，它们比LN对应物更不容易过拟合，并且当测试来自不同分布的刺激时（例如，自然场景和白色噪声之间），它们的泛化能力更好。对学习的CNN的检查揭示了几个属性。首先，与白色噪声相比，更丰富的特征图集对于预测对自然场景的响应是必要的。第二，对缓慢变化的输入的时间精确响应源自前馈抑制，类似于已知的视网膜机制。第三，在中间层中注入潜在噪声源使我们的模型能够捕获在视网膜神经节细胞中观察到的亚泊松尖峰变化。第四，通过经常性的横向连接来增强我们的CNN，使它们能够捕捉对比度适应，作为准确描述视网膜对自然场景反应的一种新兴特性。这些方法可以很容易地推广到其他感官形式和刺激合奏。总的来说，这项工作表明，CNN不仅可以准确地捕捉感官电路对自然场景的反应，还可以产生有关电路内部结构和功能的信息。
A central challenge in sensory neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission and spiking dynamics present a significant obstacle to the creation of accurate computational models of responses to natural stimuli. Here we demonstrate that deep convolutional neural networks (CNNs) capture retinal responses to natural scenes nearly to within the variability of a cell's response, and are markedly more accurate than linear-nonlinear (LN) models and Generalized Linear Models (GLMs). Moreover, we find two additional surprising properties of CNNs: they are less susceptible to overfitting than their LN counterparts when trained on small amounts of data, and generalize better when tested on stimuli drawn from a different distribution (e.g. between natural scenes and white noise). An examination of the learned CNNs reveals several properties. First, a richer set of feature maps is necessary for predicting the responses to natural scenes compared to white noise. Second, temporally precise responses to slowly varying inputs originate from feedforward inhibition, similar to known retinal mechanisms. Third, the injection of latent noise sources in intermediate layers enables our model to capture the sub-Poisson spiking variability observed in retinal ganglion cells. Fourth, augmenting our CNNs with recurrent lateral connections enables them to capture contrast adaptation as an emergent property of accurately describing retinal responses to natural scenes. These methods can be readily generalized to other sensory modalities and stimulus ensembles. Overall, this work demonstrates that CNNs not only accurately capture sensory circuit responses to natural scenes, but also can yield information about the circuit's internal structure and function.