Abstract
It is known that, in feed-forward nets, the degree of neural correlation generally increases with firing rate. Here, we study the correlations of neurons that are part of a homogeneous global feedback network, under the influence of partially correlated external input. By using numerical simulations of a network of noisy leaky integrate-and-fire neurons with delayed and smoothed spike-driven feedback, we obtain a non-monotonic relationship between the correlation coefficient and the strength of inhibitory feedback connections. This non-monotonic relationship can be explained by the interplay between the mean rate and the regularity of firing activity caused by the inhibitory feedback connections. We also show that this non-monotonic relationship is robust in both sub-threshold and supra-threshold dynamic regimes, for low and moderate internal noise levels, as well as when the network is heterogeneous. Our results point to a potent functional role for feedback as a modulator of correlated activity in neural networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
de La Rocha, J., Doiron, B., Shea-Brown, E., Josic, K., Reyes, A.: Correlation between Neural Spike Trains Increases with Firing Rate. Nature 448, 802–806 (2007)
Gutnisky, D.A., Dragoi, V.: Adaptive Coding of Visual Information in Neural Populations. Nature 452, 220–224 (2008)
Ecker, A.S., Berens, P., Keliris, G.A., Bethge, M., Logothetis, N.K., Tolias, A.S.: Decorrelated Neuronal Firing in Cortical Microcircuits. Science 327, 584–587 (2010)
Lindner, B., Doiron, B., Longtin, A.: Theory of Oscillatory Firing Induced by Spatially Correlated Noise and Delayed Inhibitory Feedback. Phys. Rev. E 72, 061919 (2005)
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., Harris, K.D.: The Asynchronous State in Cortical Circuits. Science 327, 587–590 (2010)
Tchumatchenko, T., Geisel, T., Volgushev, M., Wolf, F.: Signatures of Synchrony in Pairwise Count Correlations. Front. Comput. Neurosci. 4, 1 (2010a)
Tchumatchenko, T., Malyshev, A., Geisel, T., Volgushev, M., Wolf, F.: Correlations and Synchrony in Threshold Neuron Models. Phys. Rev. Lett. 104, 058102 (2010b)
Galan, R.F., Fourcaud-Trocme, N., Ermentrout, G.B., Urban, N.N.: Correlation-Induced Synchronization of Oscillations in Olfactory Bulb Neurons. J. Neurosci. 26, 3646–3655 (2006)
Marinazzo, D., Kappen, H.J., Gielen, S.C.A.M.: Input-Driven Oscillations in Networks with Excitatory and Inhibitory Neurons with Dynamic Synapses. Neural Comput. 19, 1739–1765 (2007)
Ostojic, S., Brunel, N., Hakim, V.: How Connectivity Background Activity and Synaptic Properties Shape the Cross Correlation between Spike Trains. J. Neurophysiol. 29, 10234–10253 (2009)
Masuda, N., Doiron, B.: Gamma Oscillations of Spiking Neural Populations Enhance Signal Discrimination. PLoS Comput. Biol. 3, e236 (2007)
Pakdaman, K., Tanabe, S., Shimokawa, T.: Coherence Resonance and Discharge Time Reliability in Neurons and Neuronal Models. Neural Networks 14, 895–905 (2001)
Lindner, B., Schimansky-Geier, L., Longtin, A.: Maximizing Spike Train Coherence or Incoherence in the Leaky Integrate-and-Fire Model. Phys. Rev. E 66, 031916 (2002)
Borgers, C., Epstein, S., Kopell, N.J.: Background Gamma Rhythmicity and Attention in Cortical Local Circuits: A Computational Study. Proc. Nat. Acad. Sci. USA 102, 7002–7007 (2005)
Bartos, M., Vida, I., Jonas, P.: Synaptic Mechanisms of Synchronized Gamma Oscillations in Inhibitory Interneurons Networks. Nat. Rev. Neurosci. 8, 45–56 (2007)
Chacron, M.J., Bastian, J.: Population Coding by Electrosensory Neurons. J. Neurophysiol. 99, 1825–1835 (2008)
Kohn, A., Smith, M.A.: Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque. J. Neurosci. 25, 3661–3673 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xie, J., Wang, Z., Zhao, J. (2014). Feedback-Dependence of Correlated Firing in Globally Coupled Networks. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-12436-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12435-3
Online ISBN: 978-3-319-12436-0
eBook Packages: Computer ScienceComputer Science (R0)