Neuroscience has focused on the detailed implementation of computation,
studying neural codes, dynamics and circuits. In machine learning, however,
artificial neural networks tend to eschew precisely designed codes, dynamics or
circuits in favor of brute force optimization of a cost function, often using
simple and relatively uniform initial architectures. Two recent developments
have emerged within machine learning that create an opportunity to connect
these seemingly divergent perspectives. First, structured architectures are
used, including dedicated systems for attention, recursion and various forms of
short- and long-term memory storage. Second, cost functions and training
procedures have become more complex and are varied across layers and over time.
Here we think about the brain in terms of these ideas. We hypothesize that (1)
the brain optimizes cost functions, (2) these cost functions are diverse and
differ across brain locations and over development, and (3) optimization
operates within a pre-structured architecture matched to the computational
problems posed by behavior. Such a heterogeneously optimized system, enabled by
a series of interacting cost functions, serves to make learning data-efficient
and precisely targeted to the needs of the organism. We suggest directions by
which neuroscience could seek to refine and test these hypotheses.

本文考虑在神经科学和机器学习之间建立联系，提出脑部通过优化各式各样的代价函数来实现数据高效学习和定向行为，其中包括关注、递归等结构体系和各种形式的短时和长时记忆存储，作者提出了未来神经科学试图改进和检验这些假设的方向。