Information coding in a laminar computational model of cat primary visual cortex

Journal of Computational Neuroscience

Volumn 34, Issue 2, 2013, Pages 273-283

  Basalyga, Gleb ^a   Montemurro, Marcelo A ^b   Wennekers, Thomas ^a  

^a UNIVERSITY OF PLYMOUTH   (United Kingdom)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Cortical microcircuit; Information coding; Phase coding; Phase of firing information; Primary visual cortex

Indexed keywords

CODES (SYMBOLS); COMPUTATION THEORY; ELECTROPHYSIOLOGY; NEURAL NETWORKS;

COMPUTATIONAL MODELLING; CORTICAL MICROCIRCUITS; DIFFERENT LAYERS; FIRING RATES; INFORMATION CODING; LOCAL FIELD POTENTIALS; NEURAL CODE; PHASE CODING; PHASE-OF-FIRING INFORMATION; PRIMARY VISUAL CORTEX;

COMPUTATIONAL METHODS;

ACTION POTENTIAL; ARTICLE; BRAIN CELL; BRAIN FUNCTION; CAT; CELL POPULATION; COMPUTATIONAL MODEL; CONTROLLED STUDY; ELECTROPHYSIOLOGY; LOCAL FIELD POTENTIAL; MATHEMATICAL MODEL; NERVE CELL CODE; NERVOUS SYSTEM ELECTROPHYSIOLOGY; NONHUMAN; PRIORITY JOURNAL; SENSORY CORTEX; SENSORY STIMULATION; SPIKE; STRIATE CORTEX;

EID: 84879694682     PISSN: 09295313     EISSN: 15736873     Source Type: Journal    
DOI: 10.1007/s10827-012-0420-x     Document Type: Article

References (62)

1. Adrian, E. (1928). The basis of sensations. New York: Norton.
2. Basalyga, G., & Wennekers, T. (2009). Large-scale computational model of cat primary visual cortex. BMC Neuroscience, 10(Suppl 1), p358.
3. Belitski, A., et al. (2008). Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information. Journal of Neuroscience, 28(22), 5696-5709.
4. Berens, P., et al. (2008). Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex. Frontiers in Systems Neuroscience, 2(2), 2.
5. Bialek, W., et al. (1991). Reading a neural code. Science, 252(5014), 1854-1857.
6. Brette, R., et al. (2007). Simulation of networks of spiking neurons: a review of tools and strategies. Journal of Computational Neuroscience, 23(3), 349-398.
7. Buzas, P., et al. (2006). Model-based analysis of excitatory lateral connections in the visual cortex. Journal of Comparative Neurology, 499(6), 861-881.
8. Carnevale, N.T., & Hines, M.L. (2006). The NEURON book. Cambridge, UK: Cambridge University Press.
9. Cover, T.M., & Thomas, J.A. (1991). Elements of information theory. New York: Wiley.
10. de Ruyter van Steveninck, R.R., et al. (1997). Reproducibility and variability in neural spike trains. Science, 275(5307), 1805-1808.
11. DeAngelis, G.C., et al. (1999). Functional micro-organization of primary visual cortex: receptive field analysis of nearby neurons. Journal of Neuroscience, 19(10), 4046-4064.
12. Destexhe, A., et al. (2001). LTS cells in cerebral cortex and their role in generating spike-and-wave oscillations. Neurocomputing, 38, 555-563.
13. Destexhe, A., et al. (1996). In vivo, in vitro, and computational analysis of dendritic calcium currents in thalamic reticular neurons. Journal of Neuroscience, 16(1), 169-185.
14. Destexhe, A., et al. (1998). Dendritic low-threshold calcium currents in thalamic relay cells. Journal of Neuroscience, 18(10), 3574-3588.
15. Engel, A.K., et al. (1990). Stimulus-dependent neuronal oscillations in cat visual cortex: inter-columnar interaction as determined by cross-correlation analysis. European Journal of Neuroscience, 2(7), 588-606.
16. Gilbert, C.D. (1977). Laminar differences in receptive field properties of cells in cat primary visual cortex. Journal of Physiology, 268(2), 391-421.
17. Grossberg, S., & Versace, M. (2008). Spikes, synchrony, and attentive learning by laminar thalamocortical circuits. Brain Research, 1218(4), 278-312.
18. Haeusler, S., & Maass, W. (2007). A statistical analysis of information processing properties of lamina-specific cortical microcircuit models. Cerebral Cortex, 17(1), 149-162.
19. Hellwig, B. (2000). A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex. Biological Cybernetics, 82(2), 111-121.
20. Hill, S., & Tononi, G. (2004). Modeling sleep and wakefulness in the thalamocortical system. Journal of Neurophysiology, 93(3), 1671-1698.
21. Hines, M.L., & Carnevale, N.T. (2008). Translating network models to parallel hardware in NEURON. Journal of Neuroscience Methods, 169(2), 425-455.
22. Hodgkin, A.L., & Huxley, A.F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), 500-544.
23. Holmgren, C., et al. (2003). Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. Journal of Physiology, 551, 139-153.
24. Holt, G.R., & Koch, C. (1999). Electrical interactions via the extracellular potential near cell bodies. Journal of Computational Neuroscience, 6(2), 169-184.
25. Izhikevich, E.M., & Edelman, G.M. (2008). Large-scale model of mammalian thalamocortical systems. Proceedings of the National Academy of Science (USA), 105(9), 3593-3598.
26. Katzner, S., et al. (2009). Local origin of field potentials in visual cortex. Neuron, 61(1), 35-41.
27. Kayser, C., et al. (2009). Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. Neuron, 61(4), 597-608.
28. Kruse, W., & Eckhorn, R. (1996). Inhibition of sustained gamma oscillations (35-80 Hz) by fast transient responses in cat visual cortex. Proceedings of the National Academy of Sciences, 93(12), 6112-6117.
29. Lindén, H., et al. (2011). Modeling the spatial reach of the LFP. Neuron, 72(5), 859-872.
30. Logothetis, N.K., et al. (2007). In vivo measurement of cortical impedance spectrum in monkeys: implications for signal propagation. Neuron, 55(5), 809-23.
31. Markram, H. (2006). The blue brain project. Nature Reviews Neuroscience, 7(2), 153-160.
32. Mazzoni, A., et al. (2011). Cortical dynamics during naturalistic sensory stimulations: experiments and models. Journal of Physiology Paris, 105(1-3), 2-15.
33. Mazzoni, A., et al. (2008). Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Computational Biology, 4(12), e1000239.
34. Migliore, M., et al. (2006). Parallel network simulations with NEURON. Journal of Computational Neuroscience, 21(1), 119-129.
35. Miikkulainen, R., et al. (2005). Computational maps in the visual cortex. Berlin, New York: Springer.
36. Montemurro, M.A., et al. (2007a). Role of precise spike timing in coding of dynamic vibrissa stimuli in somatosensory thalamus. Journal of Neurophysiology, 98(4), 1871-1882.
37. Montemurro, M.A., et al. (2007b). Tight data-robust bounds to mutual information combining shuffling and model selection techniques. Neural Computation, 19(11), 2913-2957.
38. Montemurro, M.A., et al. (2008). Phase-of-firing coding of natural visual stimuli in primary visual cortex. Current Biology, 18(5), 375-380.
39. Nordlie, E., et al. (2009). Towards reproducible descriptions of neuronal network models. PLoS Computational Biology, 5(8), e1000456.
40. O'Keefe, J., & Recce, M.L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus, 3(3), 317-330.
41. Onat, S., et al. (2011). Natural scene evoked population dynamics across cat primary visual cortex captured with voltage-sensitive dye imaging. Cerebral Cortex, 21(11), 2542-2554.
42. Panzeri, S., et al. (2007). Correcting for the sampling bias problem in spike train information measures. Journal of Neurophysiology, 98(3), 1064-1072.
43. Pettersen, K.H., & Einevoll, G.T. (2008). Amplitude variability and extracellular low-pass filtering of neuronal spikes. Biophysical Journal, 94(3), 784-802.
44. Pospischil, M., et al. (2008). Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons. Biological Cybernetics, 99(4-5), 427-441.
45. Protopapas, A.D., et al. (1999). Simulating large networks of neurons. In C. Koch, & I. Sefev (Eds.), Methods in neuronal modeling from ions to networks (chapter 12, pp. 461-498). Cambridge, MA: MIT Press.
46. Rasch, M.J., et al. (2011). Statistical comparison of spike responses to natural stimuli in monkey area V1 with simulated responses of a detailed laminar network model for a patch of V1. Journal of Neurophysiology, 105(2), 757-778.
47. Salinas, E., & Sejnowski, T.J. (2001). Correlated neuronal activity and the flow of neural information. Nature Review Neuroscience, 2(8), 539-550.
48. Shadlen, M.N., & Newsome, W.T. (1998). The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. Journal of Neuroscience, 18(10), 3870-3896.
49. Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379-423, 623-656.
50. Sillito, A.M., & Jones, H.E. (2002). Corticothalamics interactions in the transfer of visual information. Philosophical Transactions of the Royal Society London B, 357(1428), 1739-1752.
51. Skottun, B.C., et al. (1991). Classifying simple and complex cells on the basis of response modulation. Vision Research, 31(7-8), 1078-1086.
52. Strong, S.P., et al. (1998). Entropy and information in neural spike trains. Physical Review Letters, 80(1), 197-200.
53. Szymanski, F.D., et al. (2011). The laminar and temporal structure of stimulus information in the phase of field potentials of auditory cortex. Journal of Neuroscience, 31(44), 15787-15801.
54. Thomson, A.M., & Lamy, C. (2007). Functional maps of neocortical local circuitry. Frontiers in Neurocsience, 1(1), 19-42.
55. Tiesinga, P., et al. (2008). Regulation of spike timing in visual cortical circuits. Nature Reviews Neuroscience, 9(2), 97-107.
56. Traub, R.D., et al. (2005). Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. Journal of Neurophysiology, 93(4), 2194-2232.
57. Traub, R.D., & Miles, R. (1991). Neuronal networks of the hippocampus. Cambridge, UK: Cambridge University Press.
58. Tsodyks, M., et al. (1998). Neural networks with dynamic synapses. Neural Computation, 10(4), 821-835.
59. Tsodyks, M., et al. (2000). Synchrony generation in recurrent networks with frequency-dependent synapses. Journal of Neuroscience, 20(1), 1-5.
60. Ursino, M., & Cara, G.E.L. (2006). Travelling waves and EEG patterns during epileptic seizure: analysis with an integrate-and-fire neural network. Journal of Theoretical Biology, 242(1), 171-187.
61. Wohrer, A., & Kornprobst, P. (2009). Virtual retina: a biological retina model and simulator, with contrast gain control. Journal of Computational Neuroscience, 26(2), 219-249.
62. Xing, D., et al. (2009). Spatial spread of the local field potential and its laminar variation in visual cortex. Journal of Neuroscience, 29(37), 11540-11549.