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Layer-specific population rate coding in a local cortical model with a laminar structure

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Abstract

Uncovering the principle of neural coding is essential for understanding how our mysterious brain works. Recent studies have reported the laminar differences of alpha-beta and gamma rhythms in the sensory cortex, yet it remains unclear about the underlying function role of frequency-dependent interlaminar interactions in neural coding. Using a rate-based network model to simulate the cortical laminar under the external time-varying stimuli, we showed that the physiological specificity of rhythms for layers enables the cortical laminae to preferentially encode information in different frequency ranges. The interplay of the supragranular layer and infragranular layer contributes significantly to improving the neural representation of external time-varying input at the population level. Further investigations revealed the essential role of recurrent connections of the cortical laminae in regulating the population rate coding. In particular, the laminar network optimally encodes the time-varying input at intermediate strengths of intralaminar excitatory–inhibitory circuits and interlaminar connections. Additionally, we verified the crucial role of adaptation in improving population coding by introducing slow dynamics and suppressing the noise-like excitatory activity in the laminar network. These findings highlight the crucial role of frequency-dependent interlaminar interactions in encoding time-varying stimuli and may shed light on the underlying function of cortical structural specificity in neural information processing.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Panzeri, S., Macke, J.H., Gross, J., Kayser, C.: Neural population coding: combining insights from microscopic and mass signals. Trends Cogn. Sci. 19(3), 162–172 (2015)

    Article  Google Scholar 

  2. Kumar, A., Rotter, S., Aertsen, A.: Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nat. Rev. Neurosci. 11(9), 615–627 (2010)

    Article  Google Scholar 

  3. Dayan, P., Abbott, L.F.: Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT press (2005)

  4. Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity, Cambridge university press (2002)

  5. Colby, C.L., Duhamel, J.-R., Goldberg, M.E.: Ventral intraparietal area of the macaque: anatomic location and visual response properties. J. Neurophysiol. 69(3), 902–914 (1993)

    Article  Google Scholar 

  6. Kikuta, S., Fletcher, M.L., Homma, R., Yamasoba, T., Nagayama, S.: Odorant response properties of individual neurons in an olfactory glomerular module. Neuron 77(6), 1122–1135 (2013)

    Article  Google Scholar 

  7. DeWeese, M.R., Zador, A.M.: Non-gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex. J. Neurosci. 26(47), 12206–12218 (2006)

    Article  Google Scholar 

  8. Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nat. Rev. Neurosci. 1(2), 125–132 (2000)

    Article  Google Scholar 

  9. Rolls, E.T., Treves, A.: The neuronal encoding of information in the brain. Progress Neurobiol. 95(3), 448–490 (2011)

    Article  Google Scholar 

  10. Quiroga, R. Q., Panzeri, S.: Principles of Neural Coding, CRC Press (2013)

  11. Mountcastle, V.B.: The columnar organization of the neocortex. Brain: J. Neurol. 120(4), 701–722 (1997)

    Article  Google Scholar 

  12. Roopun, A.K., Middleton, S.J., Cunningham, M.O., LeBeau, F.E., Bibbig, A., Whittington, M.A., Traub, R.D.: A beta2-frequency (20–30 hz) oscillation in nonsynaptic networks of somatosensory cortex. Proc. Natl. Acad. Sci. 103(42), 15646–15650 (2006)

    Article  Google Scholar 

  13. Maier, A., Adams, G.K., Aura, C., Leopold, D.A.: Distinct superficial and deep laminar domains of activity in the visual cortex during rest and stimulation. Front. Syst. Neurosci. 4, 31 (2010)

    Google Scholar 

  14. Buffalo, E.A., Fries, P., Landman, R., Buschman, T.J., Desimone, R.: Laminar differences in gamma and alpha coherence in the ventral stream. Pro. Natl. Acad. Sci. 108(27), 11262–11267 (2011)

    Article  Google Scholar 

  15. Xing, D., Yeh, C.-I., Burns, S., Shapley, R.M.: Laminar analysis of visually evoked activity in the primary visual cortex. Proceed. National Acad. Sci. 109(34), 13871–13876 (2012)

    Article  Google Scholar 

  16. Bosman, C.A., Schoffelen, J.-M., Brunet, N., Oostenveld, R., Bastos, A.M., Womelsdorf, T., Rubehn, B., Stieglitz, T., De Weerd, P., Fries, P.: Attentional stimulus selection through selective synchronization between monkey visual areas. Neuron 75(5), 875–888 (2012)

    Article  Google Scholar 

  17. Markov, N.T., Vezoli, J., Chameau, P., Falchier, A., Quilodran, R., Huissoud, C., Lamy, C., Misery, P., Giroud, P., Ullman, S., et al.: Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex. J. Compar. Neurol. 522(1), 225–259 (2014)

    Article  Google Scholar 

  18. Rockland, K.S.: What do we know about laminar connectivity? Neuroimage 197, 772–784 (2019)

    Article  Google Scholar 

  19. Bonaiuto, J.J., Meyer, S.S., Little, S., Rossiter, H., Callaghan, M.F., Dick, F., Barnes, G.R., Bestmann, S.: Lamina-specific cortical dynamics in human visual and sensorimotor cortices. Elife 7, e33977 (2018)

    Article  Google Scholar 

  20. Mejias, J.F., Murray, J.D., Kennedy, H., Wang, X.-J.: Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex. Sci. Adv. 2(11), e1601335 (2016)

    Article  Google Scholar 

  21. Wilson, H.R., Cowan, J.D.: Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12(1), 1–24 (1972)

    Article  Google Scholar 

  22. Litwin-Kumar, A., Doiron, B.: Formation and maintenance of neuronal assemblies through synaptic plasticity. Nat. Commun. 5(1), 1–12 (2014)

    Article  Google Scholar 

  23. Szymanski, F.D., Garcia-Lazaro, J.A., Schnupp, J.W.: Current source density profiles of stimulus-specific adaptation in rat auditory cortex. J. Neurophysiol. 102(3), 1483–1490 (2009)

    Article  Google Scholar 

  24. Markram, H., Muller, E., Ramaswamy, S., Reimann, M.W., Abdellah, M., Sanchez, C.A., Ailamaki, A., Alonso-Nanclares, L., Antille, N., Arsever, S., et al.: Reconstruction and simulation of neocortical microcircuitry. Cell 163(2), 456–492 (2015)

    Article  Google Scholar 

  25. Weber, A.I., Fairhall, A.L.: The role of adaptation in neural coding. Curr. Opin. Neurobiol. 58, 135–140 (2019)

  26. Reshef, D.N., Reshef, Y.A., Finucane, H.K., Grossman, S.R., McVean, G., Turnbaugh, P.J., Lander, E.S., Mitzenmacher, M., Sabeti, P.C.: Detecting novel associations in large data sets. Science. 334(6062), 1518–1524 (2011)

    Article  MATH  Google Scholar 

  27. Albanese, D., Filosi, M., Visintainer, R., Riccadonna, S., Jurman, G., Furlanello, C.: Minerva and minepy: a c engine for the mine suite and its r, python and matlab wrappers. Bioinformatics 29(3), 407–408 (2013)

    Article  Google Scholar 

  28. Rezaei, H., Aertsen, A., Kumar, A., Valizadeh, A.: Facilitating the propagation of spiking activity in feedforward networks by including feedback. PLoS Comput. Biol. 16(8), e1008033 (2020)

    Article  Google Scholar 

  29. Buzsáki, G., Wang, X.-J.: Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012)

    Article  Google Scholar 

  30. Brunel, N., Wang, X.-J.: What determines the frequency of fast network oscillations with irregular neural discharges? i. synaptic dynamics and excitation-inhibition balance. J. Neurophysiol. 90(1), 415–430 (2003)

    Google Scholar 

  31. Spaak, E., Bonnefond, M., Maier, A., Leopold, D.A., Jensen, O.: Layer-specific entrainment of gamma-band neural activity by the alpha rhythm in monkey visual cortex. Curr. Biol. 22(24), 2313–2318 (2012)

    Article  Google Scholar 

  32. Ladenbauer, J., Augustin, M., Obermayer, K.: How adaptation currents change threshold, gain, and variability of neuronal spiking. J. Neurophysiol. 111(5), 939–953 (2014)

    Article  Google Scholar 

  33. Buzsaki, G.: Rhythms of the Brain. Oxford University Press (2006)

  34. Wang, X.-J.: Neurophysiological and computational principles of cortical rhythms in cognition. Physiological reviews 90(3), 1195–1268 (2010)

    Article  Google Scholar 

  35. Sakata, S., Harris, K.D.: Laminar structure of spontaneous and sensory-evoked population activity in auditory cortex. Neuron 64(3), 404–418 (2009)

    Article  Google Scholar 

  36. Chen, G., Gong, P.: Computing by modulating spontaneous cortical activity patterns as a mechanism of active visual processing. Nat. Commun. 10(1), 1–15 (2019)

    Article  Google Scholar 

  37. Romo, R., Brody, C.D., Hernández, A., Lemus, L.: Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399(6735), 470–473 (1999)

    Article  Google Scholar 

  38. Murray, J.D., Bernacchia, A., Roy, N.A., Constantinidis, C., Romo, R., Wang, X.-J.: Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex. Proc. Natl. Acad. Sci. 114(2), 394–399 (2017)

    Article  Google Scholar 

  39. Rossi-Pool, R., Salinas, E., Zainos, A., Alvarez, M., Vergara, J., Parga, N., Romo, R.: Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli. Proc. Natl. Acad. Sci. 113(49), E7966–E7975 (2016)

    Article  Google Scholar 

  40. Tripathy, S.J., Padmanabhan, K., Gerkin, R.C., Urban, N.N.: Intermediate intrinsic diversity enhances neural population coding. Proc. Natl. Acad. Sci. 110(20), 8248–8253 (2013)

    Article  Google Scholar 

  41. Feldmeyer, D.: Excitatory neuronal connectivity in the barrel cortex. Front. Neuroanat. 6, 24 (2012)

    Article  Google Scholar 

  42. Harris, K.D., Shepherd, G.M.: The neocortical circuit: themes and variations. Nat. Neurosci. 18(2), 170–181 (2015)

    Article  Google Scholar 

  43. Siegel, M., Donner, T.H., Engel, A.K.: Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13(2), 121–134 (2012)

    Article  Google Scholar 

  44. Bastos, A.M., Usrey, W.M., Adams, R.A., Mangun, G.R., Fries, P., Friston, K.J.: Canonical microcircuits for predictive coding. Neuron 76(4), 695–711 (2012)

    Article  Google Scholar 

  45. Roopun, A.K., LeBeau, F.E., Rammell, J., Cunningham, M.O., Traub, R.D., Whittington, M.A.: Cholinergic neuromodulation controls directed temporal communication in neocortex in vitro. Front. Neural Circuits 4, 8 (2010)

    Google Scholar 

  46. Van Kerkoerle, T., Self, M.W., Dagnino, B., Gariel-Mathis, M.-A., Poort, J., Van Der Togt, C., Roelfsema, P.R.: Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proc. Natl. Acad. Sci. 111(40), 14332–14341 (2014)

    Article  Google Scholar 

  47. Haegens, S., Barczak, A., Musacchia, G., Lipton, M.L., Mehta, A.D., Lakatos, P., Schroeder, C.E.: Laminar profile and physiology of the \(\alpha \) rhythm in primary visual, auditory, and somatosensory regions of neocortex. J. Neurosci. 35(42), 14341–14352 (2015)

    Article  Google Scholar 

  48. Bastos, A.M., Vezoli, J., Bosman, C.A., Schoffelen, J.-M., Oostenveld, R., Dowdall, J.R., De Weerd, P., Kennedy, H., Fries, P.: Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron 85(2), 390–401 (2015)

    Article  Google Scholar 

  49. Buonomano, D.V., Maass, W.: State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10(2), 113–125 (2009)

    Article  Google Scholar 

  50. Chialvo, D.: Emergent complex neural dynamics. Nat. Phys. 6, 744–750 (2010)

    Article  Google Scholar 

  51. Wilmes, K., Clopath, C.: Inhibitory microcircuits for top-down plasticity of sensory representations, Nat. Commun. 10

  52. Takembo, C.N., Mvogo, A., Ekobena Fouda, H.P., Kofané, T.C.: Effect of electromagnetic radiation on the dynamics of spatiotemporal patterns in memristor-based neuronal network. Nonlinear Dyn. 95(2), 1067–1078 (2019)

    Article  MATH  Google Scholar 

  53. Takembo, C.N., Nyifeh, P., Fouda, H.E., Kofane, T.: Modulated wave pattern stability in chain neural networks under high-low frequency magnetic radiation, p. 126891. Statistical Mechanics and its Applications, Physica A (2022)

  54. Wu, S., Guo, D.: Layer-specific neural representation in a local cortical model with a laminar structure. Int. J. Psychophysiol. 168, S233 (2021)

    Article  Google Scholar 

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Funding

This work is partly supported by National Natural Science Foundation of China (Grant Nos.31771149 and 61933003) and is partly supported by Sichuan Science and Technology Program (Grant No. 2018HH0003). Note that the data have been presented previously in abstract from [54] .

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Authors and Affiliations

  1. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Shengdun Wu, Hefei Cao, Ge Zhang, Guanyu Zhou, Elmehdi Hamouda, Yang Xia, Dezhong Yao & Daqing Guo

  2. Research Unit of Neuroinformation, Chinese Academy of Medical Sciences, Chengdu, 2019RU035, China

    Shengdun Wu, Hefei Cao, Ge Zhang, Guanyu Zhou, Elmehdi Hamouda, Yang Xia, Dezhong Yao & Daqing Guo

  3. School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Dezhong Yao

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  1. Shengdun Wu
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  2. Hefei Cao
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Correspondence to Daqing Guo.

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Wu, S., Cao, H., Zhang, G. et al. Layer-specific population rate coding in a local cortical model with a laminar structure. Nonlinear Dyn 109, 1107–1121 (2022). https://doi.org/10.1007/s11071-022-07461-z

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