Towards the neural population doctrine
Towards the neural population doctrine
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DOI：
10.1016/j.conb.2019.02.002
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发表时间：
2019-04-01
影响因子：
5.7
通讯作者：
Cunningham, John P.
中科院分区：
文献类型：
--
作者：
Saxena, Shreya;Cunningham, John P.
在整个神经科学领域，大规模数据记录和人群水平分析方法经历了爆炸性增长。虽然底层的硬件和计算技术已经得到了很好的审查，但我们在这里关注的是这些技术所实现的新科学。我们详细介绍了该领域的四个领域，其中神经种群的联合分析大大促进了我们对大脑计算的理解：相关变异性，解码，神经动力学和人工神经网络。总之，这些发现表明了一个令人兴奋的趋势，即将进入一个新时代，在这个新时代中，神经群被理解为许多大脑区域中的基本计算单元，这是一个经典的想法，已经被赋予了新的生命。
Across neuroscience, large-scale data recording and population-level analysis methods have experienced explosive growth. While the underlying hardware and computational techniques have been well reviewed, we focus here on the novel science that these technologies have enabled. We detail four areas of the field where the joint analysis of neural populations has significantly furthered our understanding of computation in the brain: correlated variability, decoding, neural dynamics, and artificial neural networks. Together, these findings suggest an exciting trend towards a new era where neural populations are understood to be the essential unit of computation in many brain regions, a classic idea that has been given new life.