论文标题
发展学习：发现尖峰网络的可解释的可塑性规则
Evolving to learn: discovering interpretable plasticity rules for spiking networks
论文作者
论文摘要
连续适应可以在不断变化的世界中生存。神经元之间的突触耦合强度的调整对于这种能力至关重要，使我们与更简单的硬连线生物区分开来。如所谓的“可塑性规则”，如何在现象学层面上数学描述这些变化对于理解生物信息处理和开发认知性能的人工系统至关重要。我们建议一种根据任务家族的定义，相关的绩效指标和生物物理约束来发现生物物理上合理的可塑性规则的自动方法。通过不断发展紧凑的符号表达式，我们确保发现的可塑性规则适合直观的理解，这是成功的沟通和人类引导的概括的基础。我们成功地将方法应用于典型的学习场景，并发现以前未知的机制从奖励中有效地学习，恢复有效的梯度散发学方法，从目标信号中学习，并通过调谐的稳态机制发现了各种功能等效的类似于STDP的规则。
Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic coupling strength between neurons are essential for this capability, setting us apart from simpler, hard-wired organisms. How these changes can be mathematically described at the phenomenological level, as so called "plasticity rules", is essential both for understanding biological information processing and for developing cognitively performant artificial systems. We suggest an automated approach for discovering biophysically plausible plasticity rules based on the definition of task families, associated performance measures and biophysical constraints. By evolving compact symbolic expressions we ensure the discovered plasticity rules are amenable to intuitive understanding, fundamental for successful communication and human-guided generalization. We successfully apply our approach to typical learning scenarios and discover previously unknown mechanisms for learning efficiently from rewards, recover efficient gradient-descent methods for learning from target signals, and uncover various functionally equivalent STDP-like rules with tuned homeostatic mechanisms.
