Biological nervous systems consist of networks of diverse, sophisticated information processors in the form of neurons of different classes. In most artificial neural networks (ANNs), neural computation is abstracted to an activation function that is usually shared between all neurons within a layer or even the whole network; training of ANNs focuses on synaptic optimization. In this paper, we propose the optimization of neuro-centric parameters to attain a set of diverse neurons that can perform complex computations. Demonstrating the promise of the approach, we show that evolving neural parameters alone allows agents to solve various reinforcement learning tasks without optimizing any synaptic weights. While not aiming to be an accurate biological model, parameterizing neurons to a larger degree than the current common practice, allows us to ask questions about the computational abilities afforded by neural diversity in random neural networks. The presented results open up interesting future research directions, such as combining evolved neural diversity with activity-dependent plasticity.

翻译：生物神经系统由不同类别神经元构成的多样化、精密信息处理器网络组成。在大多数人工神经网络中，神经计算被抽象为同一层甚至整个网络内所有神经元共享的激活函数；训练过程聚焦于突触优化。本文提出对神经中心参数进行优化，以获得具备执行复杂计算能力的多样化神经元集合。通过展示该方法的潜力，我们证明仅进化神经参数即可让智能体在无需优化任何突触权重的情况下解决各类强化学习任务。虽然本研究无意构建精确的生物学模型，但通过采用比当前普遍实践更高维度的神经元参数化方法，使我们能够探究随机神经网络中神经多样性赋予的计算能力。这些成果开辟了有趣的未来研究方向，例如将进化神经多样性与活动依赖性可塑性相结合。