Spikeling: A low-cost hardware implementation of a spiking neuron for neuroscience teaching and outreach

Lu.i - A low-cost electronic neuron for education and outreach

With increasing presence of science throughout all parts of society, there are rising expectations for researchers to effectively communicate their work and for teachers to discuss contemporary findings in their classrooms. While the community can resort to established teaching aids for the fundamental concepts of most natural sciences, there is need for similarly illustrative demonstrators in neuroscience. We therefore introduce Lu.i: a parametrizable electronic implementation of the leaky integrate-and-fire neuron model in an engaging form factor. These palm-sized neurons can be used to visualize and experience the dynamics of individual cells and small networks. When stimulated with sensory input, Lu.i demonstrates brain-inspired information processing in the hands of a student. As such, it is actively used at workshops, in classrooms, and for science communication. As a versatile tool for teaching and outreach, Lu.i nurtures the comprehension of neuroscience research and neuromorphic engineering among future generations of scientists and the general public.

Proteinoids-Polyaniline Interaction with Stimulated Neurons on Living and Plastic Surfaces

The integration of proteinoid-polyaniline (PANI) nanofibers with neuromorphic architectures shows potential for developing computer systems that are adaptable, energy-efficient, and have the capacity of tolerating faults. This work examines the capacity of proteinoid-PANI nanofibers to imitate different spiking patterns in stimulated Izhikevich neurons. The proteinoid-PANI nanofibers exhibit diverse spiking behaviors on different substrates, showcasing a broad range of control and programmability, as confirmed by experimental characterization and computational modeling. K-means clustering technique measures the extent and selectivity of the proteinoid-PANI spiking behavior in response to various stimuli and spiking patterns. The presence of strong positive correlations between membrane potential and time suggests that the system is capable of producing reliable and consistent electrical activity patterns. Proteinoid-PANI samples demonstrate enhanced stability and consistency in numerous spiking modes when compared to simulated input neurons. The results emphasize the capability of proteinoid-PANI nanofibers as a bioinspired substance for neuromorphic computing and open up possibilities for their incorporation into neuromorphic structures and bioinspired computer models.

Neurosimilator for Undergraduate Biophysics and Neurophysiology Courses

Stringent animal welfare principles are forcing undergraduate instructors to avoid the use of animals. Therefore, many hands-on lab sessions using laboratory animals are progressively replaced by computer simulations. These versatile software simulations permit the observation of the behavior of biological systems under a great variety of experimental conditions. While this versatility is important, computer simulations often work even when a student makes wrong assumptions, a situation that poses its own pedagogical problem. Hands-on learning provides pupils with the opportunity to safely make mistakes and learn organically through trial and error and should therefore still be promoted. We propose an electronic model of an excitable cell composed of different modules representing different parts of a neuron - dendrites, soma, axon and node of Ranvier. We describe a series of experiments that allow students to better understand differences between passive and active cell responses and differences between myelinated and demyelinated axons. These circuits can also be used to demonstrate temporal and spatial summation of signals coming to the neuron via dendrites, as well as the neuron coding by firing frequency. Finally, they permit experimental determination along with theoretical calculations of important biophysical properties of excitable cells, such as rheobase, chronaxie and space constant. This open-source model has been successfully integrated into an undergraduate course of the physiology of excitable cells and student feedback assessment reveals that it helped students to understand important notions of the course. Thus, this neuromorphic circuit could be a valuable tool for biophysics and neuroscience courses in other universities.

On interaction of proteinoids with simulated neural networks

Proteinoid-neuron networks combine biological neurons with spiking proteinoid microspheres, which are generated by thermal condensation of amino acids. Complex and dynamic spiking patterns in response to varied stimuli make these networks suitable for unconventional computing. This research examines the interaction of proteinoid-neuron networks with function-generator-artificial neural networks (ANN) that may create distinct electrical waveforms. Function-generator- artificial neural network (ANN) stimulates and modulates proteinoid-neuron network spiking activity and synchronisation to encode and decode information. We employ function-generator-ANN to study proteinoid-neuron network nonlinear dynamics and chaos and optimise their performance and energy efficiency. Function-generator-ANN improves proteinoid-neuron networks' computational capacities and robustness and creates unique hybrid systems with electrical devices. We address the benefits as well as the drawbacks of employing proteinoid-neuron networks for unconventional computing with function-generator-ANN.