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.
翻译:我们的目标是探索深层学习能力,以近似生物神经电路-功能连接体的瞬间功能。我们利用深层神经网络,通过从合成神经电路以及由经验支持的边界矢量细胞-光谱细胞网络中得出的发射率观测,进行了监督学习。通过一系列标准和任务,对经过培训的网络的性能进行了量化。我们的结果表明,深层神经网络能够以高精确度捕获合成生物网络的计算结果,并且数据效率很高,对生物的可塑性非常强。我们表明,经过培训的深层神经网络能够在新的环境中进行零光谱化,并允许大量任务,例如将动物在空间的位置进行高度精确的解码。我们的研究揭示了系统神经科学中的新颖而有希望的方向,并且可以通过一系列下游应用,例如目标导向的强化学习来扩展。