AI 中文总结
该研究将状态空间模型适配到脉冲网络，对共振-发放神经网络提出新解释，实现了集成STFT、循环记忆和注意力特征的脉冲兼容网络，支持并行训练且保留生物真实特征。
AI 中文摘要
状态空间模型（SSMs）为循环网络的并行训练提供了强大的理论框架。我们扩展了此前将SSMs适配到脉冲模型的工作，对共振-发放（R&F）神经网络提出了一种新解释，该解释兼容实值与脉冲输入、并行与循环执行，与超维（HD）计算有明确关联，且保留了生物学上的真实特征。我们展示了该方法的一种实现，其在单个脉冲兼容网络中集成了短时傅里叶变换（STFT）、循环记忆和注意力特征。
英文摘要
State-space models (SSMs) provide a powerful theoretical framework to enable parallel training of recurrent networks. We expand on previous work adapting SSMs to spiking models to provide a novel interpretation of resonate-and-fire (R\&F) neural networks which is compatible both with real and spiking inputs, parallel and recurrent execution, has clear connections to hyperdimensional (HD) computing, and maintains biologically-realistic features. We demonstrate an implementation of this approach which integrates an STFT, recurrent memory, and attentional features within a single spike-compatible network.
