Network bursts in cortical cultures are best simulated using pacemaker neurons and adaptive synapses

• Published in: Biological cybernetics Vol. 102; no. 4; pp. 293 - 310
• Main Authors: Gritsun, T. A, Le Feber, J, Stegenga, J, Rutten, W. L. C
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.04.2010; Springer-Verlag; Springer Nature B.V
• Subjects: Action Potentials; Adaptation, Physiological; Animals; Bioinformatics; Biological Clocks; Biomedical and Life Sciences; Biomedicine; Cells, Cultured; Complex Systems; Computer Appl. in Life Sciences; Cultured neuronal networks; Cybernetics; Electrophysiological Phenomena; Models, Neurological; Nerve Net - cytology; Nerve Net - physiology; Network bursts; Neural networks; Neurobiology; Neurons; Neurosciences; Original Paper; Pacemaker cells; Rats; Recurrent neural networks; Simulation; Synapses - physiology; Recurrent neural networks; Cultured neuronal networks; Pacemaker cells; Network bursts
• ISSN: 0340-1200; 1432-0770
• DOI: 10.1007/s00422-010-0366-x

One of the most specific and exhibited features in the electrical activity of dissociated cultured neural networks (NNs) is the phenomenon of synchronized bursts, whose profiles vary widely in shape, width and firing rate. On the way to understanding the organization and behavior of biological NNs, we reproduced those features with random connectivity network models with 5,000 neurons. While the common approach to induce bursting behavior in neuronal network models is noise injection, there is experimental evidence suggesting the existence of pacemaker-like neurons. In our simulations noise did evoke bursts, but with an unrealistically gentle rising slope. We show that a small subset of ‘pacemaker' neurons can trigger bursts with a more realistic profile. We found that adding pacemaker-like neurons as well as adaptive synapses yield burst features (shape, width, and height of the main phase) in the same ranges as obtained experimentally. Finally, we demonstrate how changes in network connectivity, transmission delays, and excitatory fraction influence network burst features quantitatively.