MEMS-cochlea: Dataset for publication

This is the dataset to the publication " Neuromorphic acoustic sensing using an adaptive microelectromechanical cochlea with integrated feedback" by Lenk et al. (DOI will follow soon).

Explanation of data:

1.) 'MEMS cochlea response to natural sound' (dataset for fig 2 in publication): In this dataset, the file "natural sound dateset" was used to drive a loudspeaker. Its given in wav-format. File named timeseries..." give the data of the response of two different sensors as well as a measurement microphone (named "input") to the wav-file. First column time in sec, second column sensor signal in V. Files named "powerspectra..." give the power spectra data of the three time series, first column frequency in Hz, second column power in absolute values not dB.

2.) 'Sensor response in dependence of feedback' (dataset for fig 3 in publication): The dataset includes the sensor signal amplitudes in mV (2nd column) as function of sound pressure amplitudes in Pa (1st column) in files with name starting "fig3a..." for different feedback strengths a_f given by the filename. Files, whose names start with "fig3b+c", give the gain (sensor amplitude active, i.e. a_f>0, divided by sensor amplitude passive, i.e. a_f=0) in the 2nd column as a function of the feedback strength a_f (1st column). In files named "fig3e_sensamp...", the sensor signal amplitude in V (2nd column) is given in dependence of the feedback strength a_f (1st column) for different driving voltages of the loudpseaker, given by the number after "loud" in the filename. If the filename says "negafnegDC", the feedback strength a_f is negativ. If it says "posafnegDC", the feedback strength was positive. The DC voltage of the feedback was always -200mV. From the dependence of sensor signal amplitude on the driving signal amplitude (both in mV), the sensitivity is extracted as the slope of the curve in mV/mV. The sensitvity is given in files, named "fig3e_sensitvity..." in the 2nd column as function of feedback strength a_f (first column).

3.) 'Comparison experiment vs model' (dataset for fig 4 in publication): These files give the values plotted in the graphs. The files, named "acrit..." contain the values of a_crit (feedback strength at bifurcation, 2nd column of file) as function of bias voltage u_DC in mV (first column of file) obtained either from experiment or from the formula (last equation in methods part). The file, named "sensitivity...", has the feedback strength a_f in the 1st column and the sensitivity in nm/Pa, obtained frome xperiments, in the 2nd column. The files, named "effective_Q_factor...", have the feedback strength a_f in 1st column and the effective Q factor, obtained from simulations, in the 2nd column. 

4.) 'Two coupled sensors' (dataset for fig 5 in publication): The files contain the frequency response, i.e. power spectral density in dB (2nd column) as function of frequency in kHz (1st column), of two different sensors for different values of the coupling strength, given by the value after "b" in the filename.

5.) 'Dynamic_adaptation_with_code' (dataset for fig 6 in publication): This dataset contains files, names starting with "timeseries...", which give the time series (sensor signal in mV vs. time in sec) for two different driving voltages of the loudspeaker (given by the value after "sound" in the filename), which are shown in fig. 6b in the publication. Files, named "envelope...", give the extracted envelope of the modelled sensor signal in V (2nd column) as function of time in sec (1st column) for different modelled sound inputs, as shown in fig 6c.  The envelope was extracted with the program "env.m", written in Matlab. The program "spice_sim" is used to start the LTSpice simulations for adaptation with different parameters. The files in the zip-archive "adapt_,8_,5_natelec" incorporates the necessary files for the LTSPice simulation of the adaptation process.