Proteinoids are thermal proteins which form microspheres in water in presence of salt. Ensembles of proteinoid microspheres exhibit passive non-linear electrical properties and active neuron-like spiking of electrical potential. We propose that various neuromorphic computing architectures can be prototyped from the proteinoid microspheres. A key feature of a neuromorphic system is a learning. Through the use of optical and resistance measurements, we study mechanisms of learning in ensembles of proteinoid microspheres. We anlyse 16 types of proteinoids, study their intrinsic morphology and electrical properties. We demonstrate that proteinoids can learn, memorize, and habituate, making them a promising candidate for novel computing.

This paper describes the use of proteinoid microspheres as an ensemble model for learning. By measuring the electrical potential of the microspheres with a Keithley 2450 source meter and storing the data in CSV files, we were able to evaluate the ability of the ensemble model to detect a change in its input. 

The electrical activity of proteinoid samples was measured using iridium needle electrodes. The sample rate was set to one sample per minute for a total of 16 samples. All measurements were performed by placing the electrodes in the solution containing the proteinoids and connecting to the source meter or the potentiostat.

The data from the electrodes was collected using a Keithey 2450 source meter in conjuction with Anapot EIS ZP 1000080 potentiostat and analyzed using Matlab, Origin Pro software.

Parameters such as voltage, current and resistance were calculated to determine the electrical properties of the proteinoid samples.

The codes of proteinoids can be seen in the paper.

In order to read the data, you need to have Microsoft Excel, Origin Pro software, and PS Trace  software installed and ready for use on your computer. It is important to make sure that you have the latest version of each of these programs for optimal performance.