Over the last few years, research was aimed to investigate neuromorphic computing methodologies for understanding the functions and behavior of biological neurons on a real-time basis. In neuron electrical models, the principles of computational neuroscience is translated on to analog hardware and the circuits reproduces the bio-physical properties of neurons. Our aim was to implement analog neuron models based on Hodgkin-Huxley formalism, and to deploy it as an educational platform for understanding the cellular and behavioral neuroscience. We have taken multiple analog hardware models and implemented its corresponding equivalent for studying the pedagogical concepts such as spiking, bursting, effects of ion channels, effect of pharmacological agents on spiking properties. We implemented remote electrical laboratories for science and engineering education bridging computing systems and neural studies. Initial implementation of the remote labs were done with commercial software which was later replaced with Free and Open Source Software (FOSS) architecture. Pedagogical analysis indicated, effectiveness in the usage of analog neuron model as a learning material for complementing neuroscience education in universities. Post-deployment studies on students and teachers includes perceived use of remote experimentation in a blended classroom scenario.
N. Nizar, Radhamani, R., Dhanush Kumar, Dr. Bipin G. Nair, Dr. Shyam Diwakar, and Dr. Krishnashree Achuthan, “Implementation of analog electrical neurons as virtual labs for neuroscience education”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017.