“Functional architecture of spontaneous cortical networks in layer 2/3 of the primary visual cortex of the mouse”, Dr Vassilis Kehayas [Jan 14, 2020]


Tuesday 14 January 2020 16:00 – 17:00 A. Payatakes Seminar Room

“Functional architecture of spontaneous cortical networks in layer 2/3 of the primary visual cortex of the mouse”

Dr Vassilis Kehayas Institute of Computer Science (ICS)

Abtract
The brain’s neocortex is a six-layered structure that consists of billions of densely interconnected neurons arranged in topographic columns. Over time much has been learned about the computational properties of single neurons of the neocortex. However, there is uncertainty in the responses of single neurons to the same stimulus. Downstream neurons must integrate activity from large neuronal populations that exhibit coordinated activity. Nevertheless, on average, neurons display close to zero correlation with each other. We remain far from understanding how networks of cortical cells coordinate and interact with each other to process information. Here we study the functional topology of cortical networks during spontaneous activity, using layer 2/3 neurons of the primary visual cortex of the mouse as our experimental model system. We used multiple datasets acquired with different calcium reporters (OGB-1, GCaMP6s) containing a large population of neurons, ranging from ~100 to ~5000 cells. Such large samples were made possible with mesoscopic two-photon imaging, which allows the near-simultaneous recording of fields of views on the order of millimetres. Our hypothesis is that cortical networks are organized into functionally linked sub-networks that we can identify by studying spontaneous activity. We used a modified version of the spike time tiling coefficient, a metric that estimates directional temporal correlation and is robust to activity fluctuations, to construct network graphs of functional correlations. These graphs exhibit considerable temporal structure across multiple scales of correlation beyond that expected from networks constructed by circularly-shifting the observed activity patterns, in which the correlations between pairs of neurons are destroyed but the inter-event interval distributions are left intact. The observed networks had more functional connections, shorter average shortest paths, and higher average clustering coefficients compared to equivalent Erdös-Rényi networks, a model of irregular structure constructed by shuffling the edges between nodes. Consistent with this evidence, the observed graphs approach a “small-world” architecture across multiple scales of correlation. Our results show that spontaneous cortical activity exhibits substantial temporal structure despite that there is little correlation on average.

Abstract
The brain’s neocortex is a six-layered structure that consists of billions of densely interconnected neurons arranged in topographic columns. Over time much has been learned about the computational properties of single neurons of the neocortex. However, there is uncertainty in the responses of single neurons to the same stimulus. Downstream neurons must integrate activity from large neuronal populations that exhibit coordinated activity. Nevertheless, on average, neurons display close to zero correlation with each other. We remain far from understanding how networks of cortical cells coordinate and interact with each other to process information. Here we study the functional topology of cortical networks during spontaneous activity, using layer 2/3 neurons of the primary visual cortex of the mouse as our experimental model system. We used multiple datasets acquired with different calcium reporters (OGB-1, GCaMP6s) containing a large population of neurons, ranging from ~100 to ~5000 cells. Such large samples were made possible with mesoscopic two-photon imaging, which allows the near-simultaneous recording of fields of views on the order of millimetres. Our hypothesis is that cortical networks are organized into functionally linked sub-networks that we can identify by studying spontaneous activity. We used a modified version of the spike time tiling coefficient, a metric that estimates directional temporal correlation and is robust to activity fluctuations, to construct network graphs of functional correlations. These graphs exhibit considerable temporal structure across multiple scales of correlation beyond that expected from networks constructed by circularly-shifting the observed activity patterns, in which the correlations between pairs of neurons are destroyed but the inter-event interval distributions are left intact. The observed networks had more functional connections, shorter average shortest paths, and higher average clustering coefficients compared to equivalent Erdös-Rényi networks, a model of irregular structure constructed by shuffling the edges between nodes. Consistent with this evidence, the observed graphs approach a “small-world” architecture across multiple scales of correlation. Our results show that spontaneous cortical activity exhibits substantial temporal structure despite that there is little correlation on average.

Have any Question or Comment?

Leave a Reply

Your email address will not be published.