  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.
