论文标题
训练有素的人工神经网络和真正的大脑中的紧急计算
Emergent Computations in Trained Artificial Neural Networks and Real Brains
论文作者
论文摘要
突触可塑性使皮层电路可以学习新任务并适应不断变化的环境。皮层电路如何使用可塑性来获取诸如决策或工作记忆之类的功能？神经元以复杂的方式连接，形成复发的神经网络，并学习改变了连接的强度。此外，神经元交流简短的离散电信号。在这里，我们描述了如何在诸如用于训练动物的神经科学实验室的动物的任务中训练复发性神经网络，以及如何在训练有素的网络中出现计算。令人惊讶的是，人造网络和真正的大脑可以使用类似的计算策略。
Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories, and how computations emerge in the trained networks. Surprisingly, artificial networks and real brains can use similar computational strategies.
