论文标题
功能连接：使用人工神经网络近似大脑网络
Functional Connectome: Approximating Brain Networks with Artificial Neural Networks
论文作者
论文摘要
我们旨在探索深度学习的能力，以近似生物神经回路实例化的功能 - 功能连接组。我们使用深层神经网络，通过从合成构建的神经回路以及经验支持的边界矢量细胞区域细胞网络中绘制的发射速率观察进行了监督学习。使用一系列标准和任务对受过训练的网络的性能进行了量化。我们的结果表明，深度神经网络能够以高精度捕获合成生物网络执行的计算，并且对生物可塑性具有很高的数据效率和鲁棒性。我们表明，受过训练的深神经网络能够在新颖的环境中执行零拍的概括，并允许大量任务，例如以高准确性地解码动物在太空中的位置。我们的研究揭示了系统神经科学的新颖而有希望的方向，可以通过多种下游应用来扩展，例如，目标指导的强化学习。
We aimed to explore the capability of deep learning to approximate the function instantiated by biological neural circuits-the functional connectome. Using deep neural networks, we performed supervised learning with firing rate observations drawn from synthetically constructed neural circuits, as well as from an empirically supported Boundary Vector Cell-Place Cell network. The performance of trained networks was quantified using a range of criteria and tasks. Our results show that deep neural networks were able to capture the computations performed by synthetic biological networks with high accuracy, and were highly data efficient and robust to biological plasticity. We show that trained deep neural networks are able to perform zero-shot generalisation in novel environments, and allows for a wealth of tasks such as decoding the animal's location in space with high accuracy. Our study reveals a novel and promising direction in systems neuroscience, and can be expanded upon with a multitude of downstream applications, for example, goal-directed reinforcement learning.
