Recent advances in deep learning optimization showed that, with some
a-posteriori information on fully-trained models, it is possible to match the
same performance by simply training a subset of their parameters. Such a
discovery has a broad impact from theory to applications, driving the research
towards methods to identify the minimum subset of parameters to train without
look-ahead information exploitation. However, the methods proposed do not match
the state-of-the-art performance, and rely on unstructured sparsely connected
models. In this work we shift our focus from the single parameters to the
behavior of the whole neuron, exploiting the concept of neuronal equilibrium
(NEq). When a neuron is in a configuration at equilibrium (meaning that it has
learned a specific input-output relationship), we can halt its update; on the
contrary, when a neuron is at non-equilibrium, we let its state evolve towards
an equilibrium state, updating its parameters. The proposed approach has been
tested on different state-of-the-art learning strategies and tasks, validating
NEq and observing that the neuronal equilibrium depends on the specific
learning setup.

通过利用神经元平衡的概念，从单个参数转向整个神经元的行为，实施参数训练，并测试不同的学习策略和任务，验证神经元平衡，并观察神经元平衡取决于特定的学习设置，从而达到与现有技术同等性能的研究。