Theoretical and Computational Neuroscience
Author: Leandro Ezequiel Fernandez | Email: email@example.com
Leandro E. Fernandez 1°, Agustín Carpio 1°, Jiamin Wu 2°, Marcelo Rozemberg 2°, Gabriel B. Mindlin 1°
1° Dpto. de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina & Instituto de Física Interdisciplinaria y Aplicada, INFINA-CONICET, Buenos Aires, Argentina.
2° Université Paris-Saclay, CNRS Laboratoire de Physique des Solides, 91405, Orsay, France.
The comprehension of neural networks emerges as a significant scientific endeavor, encompassing a diverse range of methodological approaches across various disciplines. At the heart of these networks lies a multitude of neuron models, reflecting the field’s inherent diversity.
In line with this pursuit, a recent significant advancement involves the creation of a novel ultra-compact electronic circuit that emulates the leaky integrate-and-fire dynamics. This novel development aligns harmoniously with the broader objective of understanding neural networks. By employing mathematical modeling to replicate experimental setups, we establish a structured pathway to comprehensively analyze and dissect the intricate system dynamics. This analytical framework not only reveals subtle insights beyond empirical observations but also provides a deeper grasp of the underlying principles at play.
Our mathematical model, rooted in the Morris-Lecar framework, adeptly reproduces experimental outcomes in a qualitative manner. Furthermore, it unfolds a diverse spectrum of dynamic behaviors, encompassing phenomena like bursting, single spiking, and periodic spiking. Beyond capturing the essence of neural behavior, this electronic neuron model emerges as a versatile tool for further exploration and enriched understanding, bridging the gap between theoretical insights and practical application.