System Identification of a Multi-timescale Adaptive Threshold Neuronal Model
For computational neuroscientists, this work provides a more accurate method to identify neuronal model parameters, though it is an incremental improvement over existing techniques.
The paper presents a parameter estimation method for a multi-timescale adaptive threshold neuronal model, achieving superior prediction of precise firing times compared to existing approaches, validated on synthetic and experimental rat cortical neuron data.
In this paper, the parameter estimation problem for a multi-timescale adaptive threshold (MAT) neuronal model is investigated. By manipulating the system dynamics, which comprise of a non-resetting leaky integrator coupled with an adaptive threshold, the threshold voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parametrized realizable model is then utilized inside a prediction error based framework to identify the threshold parameters with the purpose of predicting single neuron precise firing times. The iterative linear least squares estimation scheme is evaluated using both synthetic data obtained from an exact model as well as experimental data obtained from in vitro rat somatosensory cortical neurons. Results show the ability of this approach to fit the MAT model to different types of fluctuating reference data. The performance of the proposed approach is seen to be superior when comparing with existing identification approaches used by the neuronal community.