During the first part of life, the brain develops while it learns through a process called synaptogenesis. The neurons, growing and interacting with each other, create synapses. However, eventually the brain prunes those synapses. While previous work focused on learning and pruning independently, in this work we propose a biologically plausible model that, thanks to a combination of Hebbian learning and pruning, aims to simulate the synaptogenesis process. In this way, while learning how to solve the task, the agent translates its experience into a particular network structure. Namely, the network structure builds itself during the execution of the task. We call this approach Self-building Neural Network (SBNN). We compare our proposed SBNN with traditional neural networks (NNs) over three classical control tasks from OpenAI. The results show that our model performs generally better than traditional NNs. Moreover, we observe that the performance decay while increasing the pruning rate is smaller in our model than with NNs. Finally, we perform a validation test, testing the models over tasks unseen during the learning phase. In this case, the results show that SBNNs can adapt to new tasks better than the traditional NNs, especially when over $80\%$ of the weights are pruned.

翻译：在生命早期阶段，大脑在通过称为突触发生的过程进行学习的同时也在发育。神经元在生长和相互作用中产生突触，但最终大脑会对这些突触进行修剪。以往研究主要关注独立的学习与修剪过程，而本文提出了一种符合生物学合理性的模型，该模型通过赫布学习与修剪的结合，旨在模拟突触发生过程。由此，智能体在学习解决任务的过程中，将其经验转化为特定的网络结构。换言之，网络结构在执行任务期间自我构建。我们将此方法称为自构建神经网络（Self-building Neural Network, SBNN）。我们将提出的SBNN与传统的神经网络（NNs）在OpenAI的三个经典控制任务上进行了比较。结果表明，我们的模型总体表现优于传统神经网络。此外，我们观察到，随着修剪率的增加，SBNN的性能下降幅度小于传统神经网络。最后，我们进行了验证测试，将模型应用于学习阶段未接触过的任务。结果显示，SBNN比传统神经网络更能适应新任务，特别是在修剪超过80%的权重时。