Abstract
In multiunit recording with multisite neural electrodes in the brain, an adjustment of electrode parameters such as the spatial arrangement of the recording sites to the target brain region is essential to obtain good signal-to-noise ratio data. However, in the most case, the parameters have been decided according to the experience of experimenter due to the cost of animal experiments. In this study, we propose a framework to optimize the electrode parameters by the virtual experiment of multiunit recording and data analysis including spike detection and sorting of the simulated multiunit data. For the purpose, we construct the 3-D models of neural tissue and multisite electrodes. The neural tissue model is composed of sphere-shaped model neurons which are randomly arranged to avoid overlap in a cubic region. Two types of model neurons (bursting and non-bursting) are included. Each model neuron independently and randomly emits action potentials which are recorded by the model electrode with the amplitude as a function of distance between the neuron and recording site. Model parameters are determined based on the anatomical and physiological data of rat hippocampus. Five models of multisite electrodes are constructed with identical shape but different spatial arrangements of recording sites to investigate how the arrangement of recording sites, one of the electrode parameter, affects the performance of multiunit recording and spike sorting error. Virtual experiments and the performance assessments of spike sorting are conducted. It will be shown that in the case of silicon tetrode with diamond-shaped arrangement of recording sites, the number of simultaneously recorded neurons slightly increases with increasing the interval of recording sites (IRS) from 15 to 40 µm. The frequency of spike sorting error is found to be minimal for the electrode with IRS=25-35 µm. In conclusion, the proposed framework will be useful for the optimization of parameters of multisite neural electrodes.
