Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing
Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing
复制标题
深度神经网络： 生物视觉和大脑信息处理建模的新框架
DOI：
10.1146/annurev-vision-082114-035447
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发表时间：
2015-01-01
期刊：
影响因子：
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通讯作者：
Kriegeskorte, Nikolaus
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文献类型：
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作者：
Kriegeskorte, Nikolaus
神经网络建模的最新进展使计算机视觉和其他人工智能应用取得了重大进展。人类水平的视觉识别能力正在接近人工系统。人工神经网络受到大脑的启发，它们的计算可以在生物神经元中实现。卷积前馈网络现在主导着计算机视觉，它从灵长类动物视觉层次结构中获得了进一步的灵感。然而，目前的模型是为了工程目标而设计的，而不是为了模拟大脑计算。然而，最初的研究比较了这些模型和灵长类动物大脑之间的内部表征，发现了令人惊讶的相似表征空间。随着人类水平的表现不再遥不可及，我们正在进入一个令人兴奋的新时代，在这个时代，我们将能够建立生物学上忠实的前馈和循环计算模型，以了解生物大脑如何执行包括视觉在内的高水平智能。
Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.