近期神经科学领域有哪些对人工智能研究有启发的工作？
The human brain has resisted explanation for centuries, but I think there is good reason to think that we might figure out how it works within the next several  decades, for three reasons. First,  the techniques for investigating the brain are rapidly improving. Much of the hype in the last decade was about technique called fMRI that allowed only extremely course pictures of the brain, "vowels" each of which contained tens of thousands of neurons, but those techniques were never sufficient. The real action is at the level of individual neurons, and how those individual neurons work together in small circuits. We had no reliably way to study this in the last decade, but in the next decade we will. With new techniques like ontogenetic and high-density electrodes, neuroscience will advance quickly; it will be like what happened to biology when microscopes were first discovered. Second, we live in a data revolution, and the brain is a huge "Big Data" problem; a few years ago we wouldn't have even been able to store, much less analyze, the data of 80 billion neurons and trillions of connections between them, interacting over time. We soon will be able to do that, in large part using techniques that were developed to support the internet. Third, advances in AI - some that have not yet arrived -- should eventually allow machines to help us in developing a theoretical understanding of what all these neural data mean. AI is still not good enough to do true scientific reasoning, but my by hope is that we will be able to do so, before long. At that point, we may finally make some enormous breakthroughs in understanding the brain. Gray Marcus是机器之心 | GMIS 2017 全球机器智能峰会嘉宾，知乎账号由Gray Marcus授权，机器之心代为注册和运营，以上是Gray Marcus的英文版答案，以下为机器之心翻译答案供大家参考。 对大脑原理的探索已经持续了数个世纪，但我认为，我们有充分理由相信这一问题会在未来几十年得到解决，原因有三个。第一，大脑检测技术正在飞速提升。过去十年很大一部分炒作集中在应用范围非常有限的功能性磁共振成像技术。但是这一技术还远远不够。真相存在于个体神经元及其在小回路之中协作的层面。在过去的 10 年中，我们并没有研究这一问题的可靠方法；但下个 10 年情况将有所不同。通过一些新技术比如个体发育和高密度电极，神经科学会取得快速进步。这类似于显微镜的出现极大推动了生物学的发展。第二，我们身处大数据时代，大脑实际上也是一个「大数据」问题。若干年之前，我们甚至不能存储、分析 800 亿神经元及其连接之间的数据量。而现在这已不再是问题，很大的原因来自互联网技术的使用。第三，人工智能的进步——以及一些还未到来的进步——最终允许机器帮助我们从理论层面理解所有这些神经数据。人工智能依然没有达到进行真正科学推理的地步，但是我希望不久后这将会实现。到那时，也许我们最终在理解人脑方面取得了巨大突破。