Despite its better bio-plausibility, goal-driven spiking neural network (SNN) has not achieved applicable performance for classifying biological spike trains, and showed little bio-functional similarities compared to traditional artificial neural networks. In this study, we proposed the motorSRNN, a recurrent SNN topologically inspired by the neural motor circuit of primates. By employing the motorSRNN in decoding spike trains from the primary motor cortex of monkeys, we achieved a good balance between classification accuracy and energy consumption. The motorSRNN communicated with the input by capturing and cultivating more cosine-tuning, an essential property of neurons in the motor cortex, and maintained its stability during training. Such training-induced cultivation and persistency of cosine-tuning was also observed in our monkeys. Moreover, the motorSRNN produced additional bio-functional similarities at the single-neuron, population, and circuit levels, demonstrating biological authenticity. Thereby, ablation studies on motorSRNN have suggested long-term stable feedback synapses contribute to the training-induced cultivation in the motor cortex. Besides these novel findings and predictions, we offer a new framework for building authentic models of neural computation.

翻译：尽管具有更好的生物合理性，目标驱动的脉冲神经网络在分类生物脉冲序列方面尚未达到可应用的性能，并且与传统人工神经网络相比，几乎没有显示出生物功能相似性。在本研究中，我们提出了motorSRNN，一个从拓扑结构上受灵长类动物神经运动回路启发的递归脉冲神经网络。通过利用motorSRNN解码猴子初级运动皮层的脉冲序列，我们在分类准确性和能量消耗之间取得了良好的平衡。motorSRNN通过捕获和培养更多余弦调谐（运动皮层神经元的一个基本属性）与输入进行通信，并在训练过程中保持其稳定性。这种训练诱导的余弦调谐培养和持久性也在我们的猴子中观察到。此外，motorSRNN在单神经元、群体和回路水平上产生了额外的生物功能相似性，展示了生物真实性。因此，对motorSRNN的消融研究表明，长期稳定的反馈突触有助于运动皮层中训练诱导的培养。除了这些新颖的发现和预测之外，我们还为构建神经计算的真实模型提供了一个新框架。