Recent work suggests goal-driven training of neural networks can be used to
model neural activity in the brain. While response properties of neurons in
artificial neural networks bear similarities to those in the brain, the network
architectures are often constrained to be different. Here we ask if a neural
network can recover both neural representations and, if the architecture is
unconstrained and optimized, the anatomical properties of neural circuits. We
demonstrate this in a system where the connectivity and the functional
organization have been characterized, namely, the head direction circuits of
the rodent and fruit fly. We trained recurrent neural networks (RNNs) to
estimate head direction through integration of angular velocity. We found that
the two distinct classes of neurons observed in the head direction system, the
Compass neurons and the Shifter neurons, emerged naturally in artificial neural
networks as a result of training. Furthermore, connectivity analysis and
in-silico neurophysiology revealed structural and mechanistic similarities
between artificial networks and the head direction system. Overall, our results
show that optimization of RNNs in a goal-driven task can recapitulate the
structure and function of biological circuits, suggesting that artificial
neural networks can be used to study the brain at the level of both neural
activity and anatomical organization.

本文研究了神经网络在模拟神经电路的结构和功能方面的表现，通过对啮齿动物和果蝇的头部定向系统进行的研究，证明了神经网络可以自然地产生指南针神经元和移位器神经元等不同类别的神经元，其结果表明，人工神经网络可用于在神经活动和解剖组织层面上研究大脑。