On the structure of cortical microcircuits inferred from small sample sizes
| Autor: | Rodrigo Perin, Alex Roxin, Marina Vegué |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Computer science Matemàtiques i estadística::Matemàtica aplicada a les ciències [Àrees temàtiques de la UPC] Cerebral Cortex--physiology 0302 clinical medicine Microelectronics Neurociències Research Articles Network model Random graph Cerebral Cortex Brain Mapping Mathematical models General Neuroscience Statistics Multiple patch-clamp Simple random sample microcircuits Microcircuits statistics Data Interpretation Statistical clustering Deep Brain Stimulation--methods Models Neurological Sample (statistics) Microelectrònica Network topology Sensitivity and Specificity Dades -- Transmissió multiple patch-clamp Clustering 03 medical and health sciences Neural Pathways--physiology Animals Humans Cortical connectivity Spatial dependence cortical connectivity Cluster analysis Computer networks Models Statistical business.industry Reproducibility of Results Models matemàtics Pattern recognition 030104 developmental biology Sample size determination networks Sample Size Artificial intelligence Nerve Net Networks business 030217 neurology & neurosurgery Ordinadors Xarxes d' |
| Zdroj: | Recercat. Dipósit de la Recerca de Catalunya instname UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering. SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|