Exploring retinal ganglion cells encoding to multi-modal stimulation using 3D microelectrodes arrays

Microelectrode arrays (MEA) are extensively utilized in encoding studies of retinal ganglion cells (RGCs) due to their capacity for simultaneous recording of neural activity across multiple channels. However, conventional planar MEAs face limitations in studying RGCs due to poor coupling between electrodes and RGCs, resulting in low signal-to-noise ratio (SNR) and limited recording sensitivity. To overcome these challenges, we employed photolithography, electroplating, and other processes to fabricate a 3D MEA based on the planar MEA platform. The 3D MEA exhibited several improvements compared to planar MEA, including lower impedance (8.73 ± 1.66 kΩ) and phase delay (−15.11° ± 1.27°), as well as higher charge storage capacity (CSC = 10.16 ± 0.81 mC/cm2), cathodic charge storage capacity (CSCc = 7.10 ± 0.55 mC/cm2), and SNR (SNR = 8.91 ± 0.57). Leveraging the advanced 3D MEA, we investigated the encoding characteristics of RGCs under multi-modal stimulation. Optical, electrical, and chemical stimulation were applied as sensory inputs, and distinct response patterns and response times of RGCs were detected, as well as variations in rate encoding and temporal encoding. Specifically, electrical stimulation elicited more effective RGC firing, while optical stimulation enhanced RGC synchrony. These findings hold promise for advancing the field of neural encoding.

To evaluate the stability of the electrodes, we conducted impedance measurements to assess the invariance of the electrode impedance after completing retinal experiments. Specifically, we calculated the invariance of impedance as the ratio of the current impedance to the pre-experiment impedance, multiplied by 100%. We found that the majority of electrodes exhibited an impedance invariance of over 90% after one retinal experiment, indicating good stability and reusability. However, as the number of experiments increased, the amount of change in electrode impedance gradually became larger. Therefore, when the impedance change exceeds 50%, we recommend reworking the electrodes according to the process described in our paper to ensure optimal performance for reuse.

Supplementary Text 1. Calculation of CSC and CSCC and determination of electrode area
The CSC and CSCC of the microelectrode were measured in PBS solution using cyclic voltammetry (CV). The measurement employed a three-electrode system with a Pt electrode as the counter electrode and an Ag|AgCl electrode as the reference electrode. The CV scan range was -0.6V to -0.8V, and the scan rate was 50 mV/s in a slow scan mode. To calculate the CSC, the integral of the area enclosed by the current and voltage in the CV curve was divided by the microelectrode area. For the calculation of CSCC, only the area of the cathode current was integrated and divided by the microelectrode area.
In the aforementioned electrochemical system, the electrode surface area was determined using the chronoamperometry technique, which involves applying a step potential to the electrode and recording the variation of charge with time. The determination of the electrode surface area was based on the Cottrell equation, as mentioned in the literature (Fragkou et al., 2012;Baccarin et al., 2018). The Cottrell equation relates the measured charge (Q) to the square root of time (t 1/2 ) and is given by: Here, n represents the number of transferred electrons, F is the Faraday constant (96485 C eq -1 ), A is the electrode surface area (cm 2 ), C is the concentration of the redox species (mol cm -3 ), and D is the diffusion coefficient. By fitting the experimental data of charge (Q) versus t 1/2 to the Cottrell equation, the slope of the curve was determined. Taking the reciprocal of the slope yields the reciprocal of the electrode surface area, which allows us to calculate the electrode surface area accurately. Visualize the values in the binCounts matrix on a graph using a color scale. Each matrix element's value determines the color displayed at the corresponding position. Different colors can be used to represent different count values.

Supplementary Text 2. Algorithm Description for Joint ISI Distribution
By executing this algorithm, statistical analysis of the time intervals between neural spike events can be performed, and the results can be visualized as a Joint ISI Distribution graph, which displays the frequency or count of different time interval combinations.
The parameters used in this study are as follows: Min Matrix Scale = Color

Supplementary Text 3: Algorithm for Correlation Analysis
To analyze the correlation between neurons, we employed the Pearson correlation coefficient as a measure of their correlation. The Pearson correlation coefficient (r) is calculated using the following formula: Here The resulting Pearson correlation coefficient ranges between -1 and 1. A value close to 1 indicates a positive correlation, suggesting a linear relationship between the firing activities of the two neurons. A value close to -1 indicates a negative correlation, implying an inverse linear relationship. A value close to 0 indicates no linear correlation between the firing activities of the neurons.
To visually represent the correlation patterns between neurons, we constructed a correlation heatmap. The heatmap displays the correlation coefficients as color-coded values, providing an intuitive visualization of the synchronization and interactions among neurons.
In this study, we employed the aforementioned method to analyze the correlation between neurons. By calculating the Pearson correlation coefficient, we quantified the degree of correlation between neurons and represented it through a correlation heatmap. This approach offers valuable insights into the synchronization and interactions of neurons in understanding the functionality and dynamics of neural networks.

Supplementary Text 4: Algorithm for Burst Statistics
We employed the MaxInterval method to perform burst detection. The algorithm for this method is as follows: a) Start scanning the spike train and continue until an interspike interval is encountered that is less than or equal to the specified Max Interval.
b) As long as the interspike intervals remain less than or equal to the Max End Interval, include those spikes in the current burst.
c) If an interspike interval exceeds the Max End Interval, it indicates the end of the current burst. d) Merge any bursts that are separated by intervals smaller than the specified Min Interval Between Bursts. This step helps to combine closely spaced bursts into a single burst. e) Eliminate any bursts that have a duration shorter than the specified Min Duration of Burst or contain fewer spikes than the specified Min Number of Spikes. This step ensures that only bursts meeting the defined criteria are considered for analysis.
The parameters used in this study are as follows: Max