Spontaneous coordinated activity in cultured networks: Analysis of multiple ignition sites, primary circuits, and burst phase delay distributions

• Published in: Journal of computational neuroscience Vol. 24; no. 3; pp. 346 - 357
• Main Authors: Ham, Michael I., Bettencourt, Luis M., McDaniel, Floyd D., Gross, Guenter W.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.06.2008; Springer Nature B.V
• Subjects: Animals; Biomedical and Life Sciences; Biomedicine; Cell Communication; Cells, Cultured; Cerebral Cortex - physiology; Electric Impedance; Electrophysiology - methods; Gap Junctions - physiology; Human Genetics; Interneurons - physiology; Models, Neurological; Nerve Net - physiology; Neurology; Neurosciences; Reaction Time; Signal Transduction - physiology; Synapses - physiology; Synaptic Transmission - physiology; Theory of Computation; Population responses; Primary cultures; Burst patterns; Spontaneous activity; Hierarchical connectivity; Burst leaders
• ISSN: 0929-5313; 1573-6873
• DOI: 10.1007/s10827-007-0059-1

All higher order central nervous systems exhibit spontaneous neural activity, though the purpose and mechanistic origin of such activity remains poorly understood. We quantitatively analyzed the ignition and spread of collective spontaneous electrophysiological activity in networks of cultured cortical neurons growing on microelectrode arrays. Leader neurons, which form a mono-synaptically connected primary circuit, and initiate a majority of network bursts were found to be a small subset of recorded neurons. Leader/follower firing delay times formed temporally stable positively skewed distributions. Blocking inhibitory synapses usually resulted in shorter delay times with reduced variance. These distributions are characterizations of general aspects of internal network dynamics and provide estimates of pair-wise synaptic distances. The resulting analysis produced specific quantitative constraints and insights into the activation patterns of collective neuronal activity in self-organized cortical networks, which may prove useful for models emulating spontaneously active systems.