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

Emergent Bio-Functional Similarities in a Cortical-Spike-Train-Decoding Spiking Neural Network Facilitate Predictions of Neural Computation