One of the most striking capabilities behind the learning mechanisms of the
brain is the adaptation, through structural and functional plasticity, of its
synapses. While synapses have the fundamental role of transmitting information
across the brain, several studies show that it is the neuron activations that
produce changes on synapses. Yet, most plasticity models devised for artificial
Neural Networks (NNs), e.g., the ABCD rule, focus on synapses, rather than
neurons, therefore optimizing synaptic-specific Hebbian parameters. This
approach, however, increases the complexity of the optimization process since
each synapse is associated to multiple Hebbian parameters. To overcome this
limitation, we propose a novel plasticity model, called Neuron-centric Hebbian
Learning (NcHL), where optimization focuses on neuron- rather than
synaptic-specific Hebbian parameters. Compared to the ABCD rule, NcHL reduces
the parameters from $5W$ to $5N$, being $W$ and $N$ the number of weights and
neurons, and usually $N \ll W$. We also devise a "weightless" NcHL model, which
requires less memory by approximating the weights based on a record of neuron
activations. Our experiments on two robotic locomotion tasks reveal that NcHL
performs comparably to the ABCD rule, despite using up to $\sim97$ times less
parameters, thus allowing for scalable plasticity.

脑的学习机制中最引人注目的能力之一是通过结构和功能可塑性对其突触进行适应。然而，大多数用于人工神经网络的可塑性模型专注于突触而非神经元，因此优化了突触特定的 Hebbian 参数。为了克服这个限制，我们提出了一种新的可塑性模型，称为神经元为中心的 Hebbian 学习 (NcHL)，其中优化专注于神经元特定的 Hebbian 参数。和 ABCD 规则相比，NcHL 将参数数量从 5W 减少到 5N，其中 W 和 N 分别为权重和神经元的数量，通常 N 远小于 W。在两个机器人运动任务的实验证明，尽管使用的参数数量少得多（约为 97 倍），但 NcHL 与 ABCD 规则的性能相当，因此具有可扩展的可塑性。