- Featured talk presentation
- Open Access
Network community, clusters and hubs in cortical micro circuits
BMC Neuroscience volume 15, Article number: F2 (2014)
Networks of cortical neurons are essentially non-random . Although it is known that such networks show interesting structure at multiple temporal and spatial scales , almost no experimental work has been done to reveal how structures at these different scales relate to each other.
This study aimed to clarify important relations between non-randomness in groups of 3-6 neurons (clusters) and non-randomness in groups of 50-100 neurons (communities) through five steps. First, we recorded spontaneous activity of up to 500 neurons from rodent somatosensory cortex using a 512ch. multi-electrode system over one hour . Second, we reconstructed effective connectivity using transfer entropy . Third, we compared topologies of effective networks at the 3-6 neuron scale (clusters including motifs [Figure1-B]) with topologies of synaptic connections measured from 12 neuron simultaneous patch clamp experiments [5, 6]. Fourth, we constructed community or modular structures representing non-randomness from larger groups of neurons. Fifth, we evaluated the extent to which structure at each of these scales was robust. We did this by swapping connections from high degree nodes (hubs) with those from low degree nodes (non-hubs).
We found three things. First, the degree-distribution followed a power-law This demonstrated that hubs could not have been the result of random sampling from a Gaussian distribution. Second, effective networks consisting of hundreds of cortical neurons have distinctive non-random structures of connectivity at two different scales. Third, structure at the cluster level was relatively more fragile than structure at the community level. The difference between non-randomness evaluated by cluster and community will become the important first step to understand multiple different scales of cortical neuronal networks.
Song S, Sjöström PJ, Reigl M, Nelson S, Chklovskii DB: Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biology. 2005, 3 (3): e68-10.1371/journal.pbio.0030068.
Buzsáki G, Geisler C, Henze DA, Wang XJ: Interneuron Diversity series: Circuit complexity and axon wiring economy of cortical interneurons. TRENDS in Neurosciences. 2004, 27 (4): 186-193. 10.1016/j.tins.2004.02.007.
Litke AM, et al: What does the eye tell the brain?: Developent of a system for the large-scale recording of retinal output activity. IEEE Trans. Nucle. Sci. 2004, 51 (4): 1434-1440. 10.1109/TNS.2004.832706.
Schreiber T: Measuring Information Transfer. Phys. Rev. Lett. 2000, 85 (2): 461-464. 10.1103/PhysRevLett.85.461.
Perin R, Berger TK, Markram H: A synaptic organizing principle for cortical neuronal groups. Proc Natl Acad Sci USA. 2011, 108 (13): 5419-5424. 10.1073/pnas.1016051108.
Shimono M, Beggs JM: Mesoscopic neuronal activity and neuronal network architecture. Neuroscience Research. 2011, 77: e304-10.1016/j.neures.2011.07.1326.
The authors are grateful to Olaf Sporns for important suggestions, to Rodrigo de Campos Perin in the Henry Markram team at EPFL for essential advices, and to Alan Litke, Fang-Chin Yeh, Shinya Ito, Pawel Hottowy and Deborah Gunning for their all supports to accomplish this study. This study was supported by a Grant-in-Aid for JSPS Fellows for Research Abroad.
About this article
Cite this article
Shimono, M., Beggs, J.M. Network community, clusters and hubs in cortical micro circuits. BMC Neurosci 15 (Suppl 1), F2 (2014). https://doi.org/10.1186/1471-2202-15-S1-F2
- Cortical Neuron
- Neuronal Network
- Spontaneous Activity
- Somatosensory Cortex
- Degree Node