Differential effects of excitatory and inhibitory heterogeneity on the gain and asynchronous state of sparse cortical networks

Frontiers in Computational Neuroscience

Volumn 8, Issue , 2014, Pages

  Mejias, Jorge F ^a,b   Longtin, André ^b  

^a NEW YORK UNIVERSITY   (United States)

^b UNIVERSITY OF OTTAWA   (Canada)

Author keywords

Asynchronous state; Cortical networks; Gain control; Heterogeneity; Mean field; Signal detection

Indexed keywords

CELLS; CYTOLOGY; DYNAMICS; GAIN CONTROL; NEURAL NETWORKS; NEURONS;

ASYNCHRONOUS STATE; CORTICAL NETWORK; DIFFERENTIAL EFFECT; EXCITATORY NEURONS; HETEROGENEITY; INHIBITORY NEURONS; MEAN FIELD; NETWORK ACTIVITIES;

SIGNAL DETECTION;

ACTION POTENTIAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; CELL HETEROGENEITY; CELL POPULATION; CODING; CORTICAL SYNCHRONIZATION; EXCITATORY HETEROGENEITY; INFORMATION PROCESSING; INHIBITORY HETEROGENEITY; MATHEMATICAL MODEL; MEMBRANE POTENTIAL; MOLECULAR DYNAMICS; NERVE CELL; POSTSYNAPTIC MEMBRANE; PRESYNAPTIC MEMBRANE; SIGNAL DETECTION; SYNAPSE; SYNAPTIC POTENTIAL;

EID: 84907154030     PISSN: None     EISSN: 16625188     Source Type: Journal    
DOI: 10.3389/fncom.2014.00107     Document Type: Article

References (51)

1. Angelo, K., Rancz, E., Pimentel, D., Hundahl, C., Hannibal, J., Fleischmann, A., et al. (2012). A biophysical signature of network affiliation and sensory processing in mitral cells. Nature 488, 375-378. doi: 10.1038/nature11291
2. Avila-Akerberg, O., Krahe, R., and Chacron, M. (2010). Neural heterogeneities and stimulus properties affect burst coding in vivo. Neuroscience 168, 300-303. doi: 10.1016/j.neuroscience.2010.03.012
3. Bannister, N., and Larkman, A. (1995a). Dendritic morphology of CA1 pyramidal neurones from the rat hippocampus: I. branching patterns. J. Comp. Neurol. 360, 150-160. doi: 10.1002/cne.903600111
4. Bannister, N., and Larkman, A. (1995b). Dendritic morphology of CA1 pyramidal neurones from the rat hippocampus: II. spine distributions. J. Comp. Neurol. 360, 161-171. doi: 10.1002/cne.903600112
5. Borgers, C., and Kopell, N. (2003). Synchronization in networks of excitatory and inhibitory neurons with sparse, random connectivity. Neural Comput. 15, 509-538. doi: 10.1162/089976603321192059
6. Brette, R. (2012). Computing with neural synchrony. PLoS Comput. Biol. 8:e1002561. doi: 10.1371/journal.pcbi.1002561
7. Brunel, N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J. Comp. Neurosci. 8, 183-208. doi: 10.1023/A:1008925309027
8. Brunel, N., and Hakim, V. (1999). Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput. 11, 1621-1671. doi: 10.1162/089976699300016179
9. Carandini, M., and Heeger, D. (1994). Summation and division by neurons in primate visual-cortex. Science 264, 1333-1336. doi: 10.1126/science.8191289
10. Chance, F., and Abbott, L. (2000). Divisive inhibition in recurrent networks. Netw. Comput. Neur. Sys. 11, 119-129. doi: 10.1088/0954-898X/11/2/301
11. Chance, F., Abbott, L., and Reyes, A. (2002). Gain modulation from background synaptic input. Neuron 35, 773-782. doi: 10.1016/S0896-6273(02)00820-6
12. Chelaru, M. I., and Dragoi, V. (2008). Efficient coding in heterogeneous neuronal populations. Proc. Natl. Acad. Sci. U.S.A. 105, 16344-16349. doi: 10.1073/pnas.0807744105
13. Compte, A., Brunel, N., Goldman-Rakic, P. S., and Wang, X.-J. (2000). Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910-923. doi: 10.1093/cercor/10.9.910
14. de la Rocha, J., Doiron, B., Shea-Brown, E., Josic, K., and Reyes, A. (2007). Correlation between neural spike trains increases with firing rate. Nature 448, 802-806. doi: 10.1038/nature06028
15. Denker, M., Timme, M., Diesmann, M., Wolf, F., and Geisel, T. (2004). Breaking synchrony by heterogeneity in complex networks. Phys. Rev. Lett. 92, 074103. doi: 10.1103/PhysRevLett.92.074103
16. Doiron, B., Longtin, A., Berman, N., and Maler, L. (2001). Subtractive and divisive inhibition: effect of voltage-dependent inhibitory conductances and noise. Neural Comput. 13, 227-248. doi: 10.1162/089976601300014691
17. Gerstner, W. (2000). Population dynamics of spiking networks: fast transients, asynchronous states, and locking.Neural Comput. 12, 43-89. doi: 10.1162/089976600300015899
18. Gerstner, W., and Naud, R. (2009). How good are neuron models? Science 326, 379-380. doi: 10.1126/science.1181936
19. Golomb, D., Hansel, D., and Mato, G. (2001). "Mechanisms of synchrony of neural activity in large networks," inHandbook of Biological Physics, Vol. 4, eds F. Moss and S. Gielen (Amsterdam: Elsevier), 887-968.
20. Golomb, D., and Rinzel, J. (1993). Dynamics of globally coupled inhibitory neurons with heterogeneity. Phys. Rev. E 48, 4810. doi: 10.1103/PhysRevE.48.4810
21. Hausser, M., and Mel, B. (2003). Dendrites: bug or feature? Curr. Opin. Neurobiol. 13, 372-383. doi: 10.1016/S0959-4388(03)00075-8
22. Hemond, P., Epstein, D., Boley, A., Migliore, M., Ascoli, G. A., and Jaffe, D. B. (2008). Distinct classes of pyramidal cells exhibit mutually exclusive firing patterns in hippocampal area CA3b. Hippocampus 18, 411-424. doi: 10.1002/hipo.20404
23. Holt, G., and Koch, C. (1997). Shunting inhibition does not have a divisive effect on firing rates. Neural Comput. 9, 1001-1013. doi: 10.1162/neco.1997.9.5.1001
24. Hunsberger, E., Scott, M., and Eliasmith, C. (2014). The competing benefits of noise and heterogeneity in neural coding. Neural Comput. 26, 1600-1623. doi: 10.1162/NECO_a_00621
25. Jinno, S., Klausberger, T., Marton, L., Dalezios, Y., Roberts, J., Fuentealba, P., et al. (2007). Neuronal diversity in GABAergic long-range projections from the hippocampus. J. Neurosci. 27, 8790-8804. doi: 10.1523/JNEUROSCI.1847-07.2007
26. Luccioli, S., and Politi, A. (2010). Irregular collective behavior of heterogeneous neural networks. Phys. Rev. Lett. 105, 158104. doi: 10.1103/PhysRevLett.105.158104
27. Marsat, G., and Maler, L. (2010). Neural heterogeneity and efficient population codes for communication signals. J. Neurophysiol. 104, 2543-2555. doi: 10.1152/jn.00256.2010
28. Mehaffey, W. H., Doiron, B., Maler, L., and Turner, R. W. (2005). Deterministic multiplicative gain control with active dendrites. J. Neurosci. 25, 9968-9977. doi: 10.1523/JNEUROSCI.2682-05.2005
29. Mejias, J. F., and Longtin, A. (2012). Optimal heterogeneity for coding in spiking neural networks. Phys. Rev. Lett. 108, 228102. doi: 10.1103/PhysRevLett.108.228102
30. Mejias, J. F., Payeur, A., Selin, E., Maler, L., and Longtin, A. (2014). Subtractive, divisive, and non-monotonic gain control in feedforward nets linearized by noise and delays. Front. Comput. Neurosci. 8:19. doi: 10.3389/fncom.2014.00019
31. Mejias, J. F., and Torres, J. J. (2008). The role of synaptic facilitation in spike coincidence detection. J. Comput. Neurosci. 24, 222-234. doi: 10.1007/s10827-007-0052-8
32. Neltner, L., Hansel, D., Mato, G., and Meunier, C. (2000). Synchrony in heterogeneous networks of spiking neurons.Neural Comput. 12, 1607-1641. doi: 10.1162/089976600300015286
33. Nicola, W., and Campbell, S. (2013). Mean-field models for heterogeneous networks of two-dimensional integrate and fire neurons. Front. Comput. Neurosci. 7:184. doi: 10.3389/fncom.2013.00184
34. Olmi, S., Livi, R., Politi, A., and Torcini, A. (2010). Collective oscillations in disordered neural networks. Phys. Rev. E 81, 046119. doi: 10.1103/PhysRevE.81.046119
35. Padmanabhan, K., and Urban, N. (2010). Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content. Nat. Neurosci. 13, 1276-1282. doi: 10.1038/nn.2630
36. Perez, T., Mirasso, C. R., Toral, R., and Gunton, J. D. (2010). The constructive role of diversity in the global response of coupled neuron systems. Phil. Trans. R. Soc. A 368, 5619-5632. doi: 10.1098/rsta.2010.0264
37. Prescott, S. A., and De Koninck, Y. (2003). Gain control of firing rate by shunting inhibition: roles of synaptic noise and dendritic saturation. Proc. Natl. Acad. Sci. U.S.A. 100, 2076-2081. doi: 10.1073/pnas.0337591100
38. Reyes, A., Lujan, R., Rozov, A., Burnashev, N., Somogyi, P., and Sakmann, B. (1998). Target-cell specific facilitation and depression in neocortical networks. Nat. Neurosci. 1, 279-285. doi: 10.1038/1092
39. Savard, M., Krahe, R., and Chacron, M. (2011). Neural heterogeneities influence envelope and temporal coding at the sensory periphery. Neuroscience 172, 270-284. doi: 10.1016/j.neuroscience.2010.10.061
40. Sutherland, C., Doiron, B., and Longtin, A. (2009). Feedback-induced gain control in stochastic spiking networks.Biol. Cybernet. 100, 475-489. doi: 10.1007/s00422-009-0298-5
41. Talathi, S. S., Hwang, D. U., and Ditto, W. L. (2008). Spike timing dependent plasticity promotes synchrony of inhibitory networks in the presence of heterogeneity. J. Comput. Neurosci. 25, 262-281. doi: 10.1007/s10827-008-0077-7
42. Talathi, S. S., Hwang, D. U., Miliotis, A., Carney, P. R., and Ditto, W. L. (2009). Predicting synchrony in heterogeneous pulse coupled oscillators. Phys. Rev. E 80, 021908. doi: 10.1103/PhysRevE.80.021908
43. Tessone, C. J., Mirasso, C. R., Toral, R., and Gunton, J. D. (2006). Diversity-induced resonance. Phys. Rev. Lett. 97, 194101. doi: 10.1103/PhysRevLett.97.194101
44. Tripathy, S., Padmanabhan, K., Gerkin, R., and Urban, N. (2013). Intermediate intrinsic diversity enhances neural population coding. Proc. Natl. Acad. Sci. U.S.A. 110, 8248-8253. doi: 10.1073/pnas.1221214110
45. Tuckwell, H. C. (1989). Introduction to Theoretical Neurobiology. Volume 2: Nonlinear and Stochastic Theories. Cambridge, UK: Cambridge University Press.
46. Urban, N., and Tripathy, S. (2012). Circuits drive cell diversity. Nature 488, 289-290. doi: 10.1038/488289a
47. Wang, X.-J. (2001). Synaptic reverberation underlying mnemonic persistent activity. Trends Neurosci. 24, 455-463. doi: 10.1016/S0166-2236(00)01868-3
48. Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955-968. doi: 10.1016/S0896-6273(02)01092-9
49. White, J., Chow, C., Rit, J., Soto-Trevio, C., and Kopell, N. (1998). Synchronization and oscillatory dynamics in heterogeneous, mutually inhibited neurons. J. Comput. Neurosci. 5, 5-16. doi: 10.1023/A:1008841325921
50. Wong, K., and Wang, X.-J. (2006). A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci. 26, 1314-1328. doi: 10.1523/JNEUROSCI.3733-05.2006
51. Yim, M. Y., Aertsen, A., and Rotter, S. (2013). Impact of intrinsic biophysical diversity on the activity of spiking neurons. Phys. Rev. E 87, 032710. doi: 10.1103/PhysRevE.87.032710