Open Access

From dynamics to links: a sparse reconstruction of the topology of a neural network

Communications in Applied and Industrial Mathematics's Cover Image
Communications in Applied and Industrial Mathematics
Special Issue on Mathematical Models and Methods in Biology, Medicine and Physiology. Guest Editors: Michele Piana, Luigi Preziosi

Cite

1. I. Stevenson and K. Kording, How advances in neural recording affect data analysis., Nat Neurosci, vol. 14, pp. 139–142, Feb 2011.10.1038/nn.2731Search in Google Scholar

2. M. Churchland, B. Yu, M. Sahani, and S. K.V., Techniques for extracting single-trial activity patterns from large-scale neural recordings., Curr Opin Neurobiol, vol. 17, no. 5, pp. 609–618, 2007.10.1016/j.conb.2007.11.001Search in Google Scholar

3. E. Bullmore and O. Sporns, Complex brain networks: Graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience, vol. 10, no. 3, pp. 186–198, 2009.10.1038/nrn2575Search in Google Scholar

4. A. Bolstad, B. D. Van Veen, and R. Nowak, Causal network inference via group sparse regularization, IEEE Transactions on Signal Processing, vol. 59, no. 6, pp. 2628–2641, 2011.Search in Google Scholar

5. M. Winterhalder, B. Schelter, W. Hesse, K. Schwab, L. Leistritz, D. Klan, R. Bauer, J. Timmer, and W. H., Comparisson of linear signal processing techniques to infer directed interactions in multivariate neural systems., Signal Process., vol. 85, no. 11, pp. 2137–160, 2005.Search in Google Scholar

6. C. Granger, Investigating causal relations by econometric models and cross-spectral methods., Econometrica, vol. 37, pp. 424–438, 1969.10.2307/1912791Search in Google Scholar

7. S. Bressler and K. Anil, Granger causality: A well established methodology., NeuroImage, vol. 58, no. 2, pp. 323–29, 2011.10.1016/j.neuroimage.2010.02.059Search in Google Scholar

8. A. Brovelli, D. Mingzhou, A. Ledberg, Y. Chen, R. Nakamura, and B. S.L., Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality, Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 26, pp. 9849–9854, 2004.Search in Google Scholar

9. S. Kim, D. Putrino, S. Ghosh, and E. Brown, A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity, PLOS Computational Biology, vol. 7, pp. 1–13, 03 2011.10.1371/journal.pcbi.1001110Search in Google Scholar

10. A. Cadotte, T. DeMarse, P. He, and M. Ding, Causal measures of structure and plasticity in simulated and living neural networks, PLOS ONE, vol. 3, pp. 1–14, 10 2008.10.1371/journal.pone.0003355Search in Google Scholar

11. R. Vardi, A. Goldental, S. Sardi, A. Sheinin, and I. Kanter, Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity., Scientific Reports., vol. 6, p. 36228, 2016.Search in Google Scholar

12. K. Deisseroth, Optogenetics: 10 years of microbial opsins in neuroscience., Nature neuroscience, vol. 18, no. 9, pp. 1213–1225, 2015.Search in Google Scholar

13. B. Olshausen and F. D.J., Sparse coding of sensory inputs., Current Opinion in Neurobiology, vol. 14, pp. 481–487, 2004.10.1016/j.conb.2004.07.007Search in Google Scholar

14. N. Brunel, V. Hakim, P. Isope, J. Nadal, and B. B., Optimal information storage and the distribution of synaptic weights: perceptron versus purkinje cell., Neuron, vol. 43, no. 5, pp. 745–57, 2004.10.1016/S0896-6273(04)00528-8Search in Google Scholar

15. E. Bullmore and O. Sporns, The economy of brain network organization, Nature Reviews Neuroscience, vol. 13, no. 5, pp. 336–349, 2012.10.1038/nrn321422498897Search in Google Scholar

16. G. Aletti, M. Moroni, and G. Naldi, A new nonlocal nonlinear diffusion equation for image denoising and data analysis, arXiv: 1707.06396, 2017.Search in Google Scholar

17. G. Palazzolo, M. Moroni, A. Soloperto, G. Aletti, G. Naldi, M. Vassalli, T. Nieus, and F. Difato, Fast wide-volume functional imaging of engineered in vitro brain tissues, Scientific Reports, vol. 7, no. 1, 2017.10.1038/s41598-017-08979-8556122728819205Search in Google Scholar

18. E. D’Angelo, T. Nieus, A. Maffei, S. Armano, P. Rossi, V. Taglietti, A. Fontana, and N. G., Theta-frequency bursting and resonance in cerebellar granule cells: Experimental evidence and modeling of a slow k+-dependent mechanism, Journal of Neuroscience, vol. 21, no. 3, pp. 759–770, 2001.10.1523/JNEUROSCI.21-03-00759.2001Search in Google Scholar

19. T. Nieus, E. Sola, J. Mapelli, E. Saftenku, P. Rossi, and D. E., Ltp regulates burst initiation and frequency at mossy fiber-granule cell synapses of rat cerebellum: experimental observations and theoretical predictions., J Neurophysiol, vol. 95, pp. 686–699, Feb 2006.10.1152/jn.00696.200516207782Search in Google Scholar

20. M. Garofalo, T. Nieus, P. Massobrio, and M. S., Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks., PLoS One, vol. 4, no. 8, p. e6482, 2009.10.1371/journal.pone.0006482271586519652720Search in Google Scholar

21. A. Maccione, M. Garofalo, T. Nieus, M. Tedesco, L. Berdondini, and S. Martinoia, Multiscale functional connectivity estimation on low-density neuronal cultures recorded by high-density cmos micro electrode arrays., J Neurosci Methods, vol. 207, pp. 161–171, Jun 2012.10.1016/j.jneumeth.2012.04.00222516778Search in Google Scholar

22. S. Ullo, T. Nieus, D. Sona, A. Maccione, L. Berdondini, and M. V., Functional connectivity estimation over large networks at cellular resolution based on electrophysiological recordings and structural prior., Front Neuroanat, vol. 8, p. 137, 2014.10.3389/fnana.2014.00137423836725477790Search in Google Scholar

23. S. Song, P. Sjostrom, M. Reigl, S. Nelson, and C. D.B., Highly nonrandom features of synaptic connectivity in local cortical circuits., PLoS Biol, vol. 3, p. e68, Mar 2005.10.1371/journal.pbio.0030068105488015737062Search in Google Scholar

24. Y. Gong, C. Huang, J. Li, Z. Jin, B. Grewe, Y. Zhang, S. Eismann, and S. M., High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor., Science (New York, N.Y.), vol. 350, pp. 1361–1366, Dec 2015.Search in Google Scholar

eISSN:
2038-0909
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Mathematics, Numerical and Computational Mathematics, Applied Mathematics