Abstract
Systems-level neurophysiological data reveal coherent activity that is distributed across large regions of cortex. This activity is often thought of as an emergent property of recurrently connected networks. The fact that this activity is coherent means that populations of neurons may be thought of as the carriers of information, not individual neurons. Therefore, systems-level descriptions of functional activity in the network often find their simplest form as combinations of the underlying neuronal variables. In this paper, we provide a general framework for constructing low-dimensional dynamical systems that capture the essential systems-level information contained in large-scale networks of neurons. We demonstrate that these dimensionally-reduced models are capable of predicting the response to previously un-encountered input and that the coupling between systems-level variables can be used to reconstruct cellular-level functional connectivities. Furthermore, we show that these models may be constructed even in the absence of complete information about the underlying network.
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Acknowledgments
This work was supported by (ATS) NIH NIBIB005432 and (LT) NSF DMS-0506257. ATS would like to thank Liping Wei and the Center for Bioinformatics at the College of Life Sciences at Peking University for their hospitality. Most of the large-scale numerical computations were performed on the NJIT Hydra cluster obtained under NSF MRI-0420590.
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Supplementary Movie 1
Comparison of network activity in a large-scale simulation driven by 16 orientations (left hand movie), and predictions by a DRM trained on all 16 orientations (middle movie) and by a DRM trained on only 8 orientations (right hand movie). (MOV 3049 kb)
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Tao, L., Sornborger, A.T. Dimensionally-reduced visual cortical network model predicts network response and connects system- and cellular-level descriptions. J Comput Neurosci 28, 91–106 (2010). https://doi.org/10.1007/s10827-009-0189-8
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DOI: https://doi.org/10.1007/s10827-009-0189-8