A Bayesian Decoder Representing Single-Directional Connectivity between Neurons in Brain-Machine Interface

Directional neural connectivity is essential to understanding how neurons encode and transmit information in the neural network. The previous studies on single neuronal encoding models illustrate how the neurons modulate the stimulus, underlying movement, and interactions with other neurons. And these encoding models have been used in the Bayesian decoders of the brain-machine interface (BMI) to explain how the neural population represents the movement intentions. However, the existing methods only consider rough correlations between neurons without directional connections, while the synapses between real neurons have explicit directions. Therefore, in these models, we cannot specify the proper functional neural connectivity and how the neurons cooperate to represent the movement intentions in truth. Therefore, we propose representing the directional neural connectivity in the Bayesian decoder in BMI. Our method derives a chain-likelihood based on Bayes' rule to form the single-directional influence between neurons. According to the derived structure, the prior causality relationship can be used to build more precise neural encoding models. Therefore, our method can represent the functional neural circuit more precisely and benefit the decoding in the BMI. We validate the proposed method in synthetic data simulating the rat's two-lever discrimination task. The results demonstrate that our method outperforms the existing methods by representing directional-neural connectivity. Besides, our method is more efficient in training because it employs fewer parameters. Consequently, our method can be used to evaluate the causality between neurons at the behavior level.