Throughout history, the development of artificial intelligence, particularly
artificial neural networks, has been open to and constantly inspired by the
increasingly deepened understanding of the brain, such as the inspiration of
neocognitron, which is the pioneering work of convolutional neural networks.
Per the motives of the emerging field: NeuroAI, a great amount of neuroscience
knowledge can help catalyze the next generation of AI by endowing a network
with more powerful capabilities. As we know, the human brain has numerous
morphologically and functionally different neurons, while artificial neural
networks are almost exclusively built on a single neuron type. In the human
brain, neuronal diversity is an enabling factor for all kinds of biological
intelligent behaviors. Since an artificial network is a miniature of the human
brain, introducing neuronal diversity should be valuable in terms of addressing
those essential problems of artificial networks such as efficiency,
interpretability, and memory. In this Primer, we first discuss the
preliminaries of biological neuronal diversity and the characteristics of
information transmission and processing in a biological neuron. Then, we review
studies of designing new neurons for artificial networks. Next, we discuss what
gains can neuronal diversity bring into artificial networks and exemplary
applications in several important fields. Lastly, we discuss the challenges and
future directions of neuronal diversity to explore the potential of NeuroAI.

论文介绍了神经多样性对于人工神经网络的重要意义，并探讨了如何将神经多样性引入人工网络以解决其固有问题，例如效率、可解释性和记忆问题。

走向神经人工智能：将神经多样性引入人工神经网络

Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks

We introduce Divnet, a flexible technique for learning networks with diverse
neurons. Divnet models neuronal diversity by placing a Determinantal Point
Process (DPP) over neurons in a given layer. It uses this DPP to select a
subset of diverse neurons and subsequently fuses the redundant neurons into the
selected ones. Compared with previous approaches, Divnet offers a more
principled, flexible technique for capturing neuronal diversity and thus
implicitly enforcing regularization. This enables effective auto-tuning of
network architecture and leads to smaller network sizes without hurting
performance. Moreover, through its focus on diversity and neuron fusing, Divnet
remains compatible with other procedures that seek to reduce memory footprints
of networks. We present experimental results to corroborate our claims: for
pruning neural networks, Divnet is seen to be notably superior to competing
approaches.

本文介绍了一种名为 Divnet 的灵活技术，用于学习具有不同神经元的网络，并通过确定性点过程（DPP）和神经元融合的方法来捕获神经元的多样性和实现网络架构的自动优化和缩小，该方法针对剪枝神经网络问题显示优越性。