International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 3, No. 4, 8 Jun. 2011
Cover page and Table of Contents: PDF (size: 358KB)
Effect of Short-Term Synaptic Plasticity on Correlated firing in Feedback Networks
Full Text (PDF, 358KB), PP.18-24
Views: 0 Downloads: 0
Author(s)
Jinli Xie
1,*
Zhijie Wang
1
Haibo Shi
1
Index Terms
Correlation, oscillation, feedback, short-term synaptic plasticity
Abstract
The firing activity of a neuronal population is correlated, which has been linked to information coding and exchanging. Short-term synaptic plasticity allows synapses to increase (facilitate) or decrease (depress) over a wide range of time scales. It is critical to understand the characteristics and mechanisms of the correlated firing and the role of short-term synaptic plasticity in regulating firing activity. The short-term synaptic depression and facilitation are examined at the synapses in the inhibitory feedback loop of feedback neural networks. Numerical simulations reveal that the modulation of the correlated firing by dynamics of depression and facilitation is due to their effects on the synaptic strength. By varying synaptic time constants, the enhancement in either firing rate or intensity of oscillations can improve the correlated firing. Our study thus provides a general computational analysis of the sequential interaction of short-term plasticity with neuronal firing.
Cite This Paper
Jinli Xie, Zhijie Wang, Haibo Shi, "Effect of Short-Term Synaptic Plasticity on Correlated firing in Feedback Networks", International Journal of Computer Network and Information Security(IJCNIS), vol.3, no.4, pp.18-24, 2011. DOI:10.5815/ijcnis.2011.04.03
Reference
[1]E. Salinas, T. J. Sejnowski, "Correlated neuronal activity and the flow of neural information," Nature Rev. Neurosci, 2001, vol. 2, pp. 539-549.
[2]M. J. Chacron, J. Bastian. “Population coding by electrosensory neurons,” J. Neurophysiol. 2008, 99: 1825-1835.
[3]I. Ginzburg, H. Sompolinsky, "Theory of correlations in stochastic neural networks," Phys. Rev. E, 1994, vol. 50, pp. 3171-3190.
[4]N. Masuda , B. Doiron. “Gamma oscillations of spiking neural populations enhance signal discrimination,” PLoS Comput Biol. 2007, 3: e236.
[5]Dong, Y., Mihalas, S., Qiu, F., von der Heydt, R., and Niebur, E. “Synchrony and the binding problem in macaque visual cortex,” J. Vis. 2008, 8: 1–16.
[6]J. Niessing, B. Ebisch, K.E.Schmidt, M.Niessing, W.Singer, and R.A.W.Galuske. “Hemodynamic signals correlate tightly with synchronized gamma oscillations,” Science. 2005, 309: 948–951.
[7]Tchumatchenko T., Geisel T., Volgushev M., and Wolf F. “Signatures of synchrony in pairwise count correlations,” Front. Comput. Neurosci. 2010, 4,1.
[8]G. B. Ermentrout, R. F. Galan, and N. N.Urban. “Reliability, synchrony and noise,” Trends Neurosci. 2008, 31: 428–434.
[9]Shea-Brown E, Josic´ K, de la Rocha J, and Doiron B. “Correlation and synchrony transfer in integrate-and-fire neurons: basic properties and consequences for coding,” Phys Rev Lett. 2008, 100:108102.
[10]MA Smith, A Kohn, “Spatial and temporal scales of neuronal correlation in primary visual cortex,” J. Neurosci., 2008, vol. 28, pp. 12591–12603.
[11]J. de La Rocha, B. Doiron, E. Shea-Brown, K. Josic, and A. Reyes, "Correlation between neural spike trains increases with firing rate," Nature, 2007, vol. 448, pp. 802-806.
[12]K. Josic, E. Shea-Brown, B. Doiron, and J. De La Rocha. “Stimulus-dependent correlations and population codes,” Neural Comput. 2009, 21:2774-2804.
[13]W. Bair, E. Zohary, and W. T. Newsome, “Correlated firing in macaque visual area MT: Time scales and relationship to behavior,” J. Neurosci., 2001, vol. 21, pp. 1676-1697.
[14]A. Kohn, and M. A. Smith, “Stimulus dependence of neuronal correlation in primary visual cortex of the macaque,” J. Neurosci., 2005, vol. 25, pp. 3661-3673.
[15]R. F. Galan, N. Fourcaud-Trocme, G. B. Ermentrout, and N. N. Urban, "Correlation induced synchronization of oscillations in olfactory bulb neurons," J. Neurosci., 2006, vol. 26, pp. 3646–3655.
[16]S. Ostojic, N. Brunel, and V. Hakim. “How connectivity background activity and synaptic properties shape the cross correlation between spike trains,” J. Neurophysiol. 2009, 29: 10234-10253.
[17]A. S. Ecker, P. Berens, G. A. Keliris,M. Bethge, N. K. LogothetiS, and A. S. Tolias. “Decorrelated Neuronal Firing in Cortical Microcircuits,” Science. 2010. 327: 584-587
[18]Akerberg, O.A., and Chacron, M.J. “Coding signal strength by correlated activity in bursting neurons,” BMC Neuroscience 11 (Suppl 1), 2010, F3.
[19]Tchumatchenko, T., Malyshev, A., Geisel, T., Volgushev, M., and Wolf, F. “Correlations and synchrony in threshold neuron models,” Phys. Rev. Lett. 2010, 104, 058102.
[20]L. F. Abbott, W. G. Regehr, “Synaptic computation,” Nature, 2004, vol. 431, pp. 796-803.
[21]K. Szalisznyo, A. Longtin, and L. Maler, “Effect of synaptic plasticity on sensory coding and steady-state filtering properties in the electric sense,” BioSystems, 2008, vol. 92, pp. 16-28.
[22]B. Lindner, D. Gangloff, A. Longtin, and J. E. Lewis, “Broadband coding with dynamic synapses,” J. Neurosci., 2009, vol. 29, pp. 2076-2088.
[23]J. E. Lewis, and L. Maler, “Synaptic dynamics on different time scales in a parallel fiber feedback pathway of the weakly electric fish,” J. Neurophysiol, 2004, vol. 91, pp. 1064-1070.
[24]Tsodyks, M., Uziel, A., and Markram, H. “Synchrony generation in recurrent networks with frequency-dependent synapses,” J. Neurosci., 2000, 20(RC50), 1–5.
[25]Pantic, L., Torres, J. J., and Kappen, H. J. “Coincidence detection with dynamicsynapses,” Network Computation in Neural Systems, 2003, 14(1), 17–33.
[26]M. Bartos, I. Vida, and P. Jonas. “Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneurons networks,” Nature Rev. Neurosci, 2007, vol. 8, pp. 45-56.
[27]D. Marinazzo, H. J. Kappen, and S. C. A. M. Gielen, “Input-driven oscillations in networks with excitatory and inhibitory neurons with dynamic synapses,” Neural Comput., 2007, vol. 19, pp. 1739-1765.