Predicting Spike Features of Hodgkin-Huxley-Type Neurons With Simple Artificial Neural Network

Hodgkin-Huxley (HH)-type model is the most famous computational model for simulating neural activity. It shows the highest accuracy in capturing neuronal spikes, and its model parameters have definite physiological meanings. However, HH-type models are computationally expensive. To address this problem, a previous study proposed a spike prediction module (SPM) to predict whether a spike will take place 1 ms later based on three voltage values with intervals of 1 ms. Although SPM does well, it fails to evaluate the informative features of the spike. In this study, the feature prediction module (FPM) based on simple artificial neural network (ANN) was proposed to predict spike features including maximum voltage, minimum voltage, and dropping interval. Nine different HH-type models were adopted whose firing patterns cover most of the firing behaviors observed in the brain. Voltage and spike feature samples under constant external input current were collected for training and testing. Experiment results illustrated that the combination of SPM and FPM can accurately predict the spiking part of different HH-type models and can generalize to unseen types of input current. The combination of SPM and FPM may offer a possible way to simulate the action potentials of biological neurons with high accuracy and efficiency.

Automated summary

This study proposed a feature prediction module based on a simple artificial neural network to predict specific features of neuronal spikes. Experimental results demonstrated that the combination of the feature prediction module and the spike prediction module accurately predicts the spiking behavior of different types of HH-type models and can generalize to unseen types of input current. The integration of the two modules offers a potential way to simulate the action potentials of biological neurons with high accuracy and efficiency.