The differentiation of neural activity in the primary visual cortex (V1) for the edges and surface regions of stimuli is a key feature in representing visual information. This differentiation is closely related to the phenomenon of surface inhibition, where neural activity in central surface regions is suppressed relative to edges of square stimuli. However, due to the challenges in manipulating biological experiments and the limitations of simplified models, the detailed synaptic-level analysis of the regulatory mechanisms underlying this differentiation has not been revealed. In this study, a visual information transmission pathway from visual stimuli to the lateral geniculate nucleus (LGN) of the thalamus, and further to the input and output layers of V1, is constructed by using real biological anatomical data to investigate the regulatory mechanisms underlying the aforementioned neural activity differentiation. The model successfully replicates the surface inhibition characteristics observed in biological experiments: the input layer exhibits relatively uniform responses to both surface and edge stimuli, while the output layer shows strong activity in edge regions and suppressed activity in central surface regions, creating a "hole" effect. Through control experiments—specifically, by eliminating long-range connections in L2/3—we find that long-range connections in layer 2/3 of V1 are the necessary conditions for generating the surface inhibition phenomenon within this layer. Furthermore, modulating either the proportion or the spatial distribution of long-range connections in L2/3 can exert regulatory effects on the surface inhibition phenomenon. This not only facilitates our understanding of the neural mechanisms underlying visual processing but also demonstrates the advantages of computational modeling in elucidating internal cortical mechanisms that are difficult to manipulate in biological experiments.
Citation: Peihan Wang, Fang Han, Hao Yang. Effect of long-range connections on surface inhibition in V1 area based on large-scale neural network modeling[J]. Electronic Research Archive, 2025, 33(12): 7999-8018. doi: 10.3934/era.2025352
The differentiation of neural activity in the primary visual cortex (V1) for the edges and surface regions of stimuli is a key feature in representing visual information. This differentiation is closely related to the phenomenon of surface inhibition, where neural activity in central surface regions is suppressed relative to edges of square stimuli. However, due to the challenges in manipulating biological experiments and the limitations of simplified models, the detailed synaptic-level analysis of the regulatory mechanisms underlying this differentiation has not been revealed. In this study, a visual information transmission pathway from visual stimuli to the lateral geniculate nucleus (LGN) of the thalamus, and further to the input and output layers of V1, is constructed by using real biological anatomical data to investigate the regulatory mechanisms underlying the aforementioned neural activity differentiation. The model successfully replicates the surface inhibition characteristics observed in biological experiments: the input layer exhibits relatively uniform responses to both surface and edge stimuli, while the output layer shows strong activity in edge regions and suppressed activity in central surface regions, creating a "hole" effect. Through control experiments—specifically, by eliminating long-range connections in L2/3—we find that long-range connections in layer 2/3 of V1 are the necessary conditions for generating the surface inhibition phenomenon within this layer. Furthermore, modulating either the proportion or the spatial distribution of long-range connections in L2/3 can exert regulatory effects on the surface inhibition phenomenon. This not only facilitates our understanding of the neural mechanisms underlying visual processing but also demonstrates the advantages of computational modeling in elucidating internal cortical mechanisms that are difficult to manipulate in biological experiments.
| [1] |
J. Rodrigues, J. M. du Buf, Multi-scale lines and edges in V1 and beyond: brightness, object categorization and recognition, and consciousness, Biosystems, 95 (2009), 206–226. https://doi.org/10.1016/j.biosystems.2008.10.006 doi: 10.1016/j.biosystems.2008.10.006
|
| [2] |
S. Celebrini, S. Thorpe, Y. Trotter, M. Imbert, Dynamics of orientation coding in area V1 of the awake primate, Visual Neurosci., 10 (1993), 811–825. https://doi.org/10.1017/s0952523800006052 doi: 10.1017/s0952523800006052
|
| [3] |
H. S. Scholte, J. Jolij, H. Spekreijse, V. A. F. Lamme, Neural correlates of texture boundary detection and surface segregation are present in human V1, J. Vision, 3 (2003), 243. https://doi.org/10.1167/3.9.243 doi: 10.1167/3.9.243
|
| [4] |
D. M. Alexander, C. V. Leeuwen, Mapping of contextual modulation in the population response of primary visual cortex, Cognit. Neurodyn., 4 (2010), 1–24. https://doi.org/10.1007/s11571-009-9098-9 doi: 10.1007/s11571-009-9098-9
|
| [5] |
G. Zurawel, I. Ayzenshtat, S. Zweig, R. Shapley, H. Slovin, A contrast and surface code explains complex responses to black and white stimuli in V1, J. Neurosci., 34 (2014), 14388–14402. https://doi.org/10.1523/JNEUROSCI.0848-14.2014 doi: 10.1523/JNEUROSCI.0848-14.2014
|
| [6] |
Y. Yang, T. Wang, Y. Li, W. Dai, G. Yang, C. Han, et al., Coding strategy for surface luminance switches in the primary visual cortex of the awake monkey, Nat. Commun., 13 (2022), 286. https://doi.org/10.1038/s41467-021-27892-3 doi: 10.1038/s41467-021-27892-3
|
| [7] |
Z. Shay, G. Guy, R. Shapley, H. Slovin, Representation of color surfaces in V1: edge enhancement and unfilled holes, J. Neurosci., 35 (2015), 12103–12115. https://doi.org/10.1523/JNEUROSCI.1334-15.2015 doi: 10.1523/JNEUROSCI.1334-15.2015
|
| [8] |
E. Baspinar, A. Sarti, G. Citti, A sub-Riemannian model of the visual cortex with frequency and phase, J. Math. Neurosci., 10 (2020), 11. https://doi.org/10.1186/s13408-020-00089-6 doi: 10.1186/s13408-020-00089-6
|
| [9] |
G. Chen, F. Schuessler, W. Metzner, A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing, Sci. Adv., 8 (2022). https://doi.org/10.1126/sciadv.abq7592 doi: 10.1126/sciadv.abq7592
|
| [10] |
Y. N. Billeh, B. Cai, S. L. Gratiy, K. Dai, R. Iyer, N. W. Gouwens, et al., Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex, Neuron, 106 (2020), 388–403. https://doi.org/10.1016/j.neuron.2020.01.040 doi: 10.1016/j.neuron.2020.01.040
|
| [11] |
H. Yang, F. Han, Q. Wang, A large-scale neuronal network modelling study: stimulus size modulates gamma oscillations in the primary visual cortex by long-range connections, Eur. J. Neurosci., 60 (2024), 4224–4243. https://doi.org/10.1111/ejn.16429 doi: 10.1111/ejn.16429
|
| [12] |
H. Yang, P. Wang, F. Han, Q. Wang, An interpretable mechanism for grating-induced cross-inhibition and gamma oscillation based on a visual cortical neuronal network model, Electron. Res. Arch., 32 (2024), 2936–2954. https://doi.org/10.3934/era.2024134 doi: 10.3934/era.2024134
|
| [13] |
J. Antolík, R. Cagnol, T. Rózsa, C. Monier, Y. Frégnac, A. P. Davison, et al., A comprehensive data-driven model of cat primary visual cortex, PLoS Comput. Biol., 20 (2024), e1012342. https://doi.org/10.1371/journal.pcbi.1012342 doi: 10.1371/journal.pcbi.1012342
|
| [14] |
Y. Jiang, G. Purushothaman, V. A. Casagrande, The functional asymmetry of ON and OFF channels in the perception of contrast, J. Neurophysiol., 114 (2015), 2816–2829. https://doi.org/10.1152/jn.00560.2015 doi: 10.1152/jn.00560.2015
|
| [15] |
Y. J. Cao, C. Lin, Y. J. Pan, H. Zhao, Application of the center–surround mechanism to contour detection, Multimed. Tools Appl., 78 (2019), 25121–25141. https://doi.org/10.1007/s11042-019-7722-1 doi: 10.1007/s11042-019-7722-1
|
| [16] |
E. A. Allen, R. D. Freeman, Dynamic spatial processing originates in early visual pathways, J. Neurosci., 26 (2006), 11763–11774. https://doi.org/10.1523/JNEUROSCI.3297-06.2006 doi: 10.1523/JNEUROSCI.3297-06.2006
|
| [17] |
U. Bauer, M. Scholz, J. B. Levitt, K. Obermayer, J. S. Lund, A model for the depth-dependence of receptive field size and contrast sensitivity of cells in layer 4C of macaque striate cortex, Vision Res., 39 (1999), 613–629. https://doi.org/10.1016/s0042-6989(98)00172-2 doi: 10.1016/s0042-6989(98)00172-2
|
| [18] |
Y. Gonchar, A. Burkhalter, Distinct GABAergic targets of feedforward and feedback connections between lower and higher areas of rat visual cortex, J. Neurosci., 23 (2003), 10904–10912. https://doi.org/10.1523/JNEUROSCI.23-34-10904.2003 doi: 10.1523/JNEUROSCI.23-34-10904.2003
|
| [19] |
X. Tie, S. Li, Y. Feng, B. Lai, S. Liu, B. Jiang, Distinct roles of NMDAR and mGluR5 in light exposure reversal of feedforward synaptic strength in V1 of juvenile mice after binocular vision deprivation, Neuroscience, 384 (2018), 131–138. https://doi.org/10.1016/j.neuroscience.2018.05.034 doi: 10.1016/j.neuroscience.2018.05.034
|
| [20] |
A. Alreja, I. Nemenman, C. J. Rozell, Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices, PLoS Comput. Biol., 18 (2022), e1009642. https://doi.org/10.1371/journal.pcbi.1009642 doi: 10.1371/journal.pcbi.1009642
|
| [21] |
H. Markram, M. Toledo-Rodriguez, Y. Wang, A. Gupta, G. Silberberg, C. Wu, Interneurons of the neocortical inhibitory system, Nat. Rev. Neurosci., 5 (2004), 793–807. https://doi.org/10.1038/nrn1519 doi: 10.1038/nrn1519
|
| [22] |
N. Fourcaud-Trocmé, D. Hansel, C. van Vreeswijk, N. Brunel, How spike generation mechanisms determine the neuronal response to fluctuating inputs, J. Neurosci., 23 (2003), 11628–11640. https://doi.org/10.1523/JNEUROSCI.23-37-11628.2003 doi: 10.1523/JNEUROSCI.23-37-11628.2003
|
| [23] |
D. N. Scott, M. J. Frank, Adaptive control of synaptic plasticity integrates micro-and macroscopic network function, Neuropsychopharmacology, 48 (2023), 121–144. https://doi.org/10.1038/s41386-022-01374-6 doi: 10.1038/s41386-022-01374-6
|
| [24] |
C. Rivadulla, J. Sharma, M. Sur, Specific roles of NMDA and AMPA receptors in direction-selective and spatial phase-selective responses in visual cortex, J. Neurosci., 21 (2001), 1710–1719. https://doi.org/10.1523/JNEUROSCI.21-05-01710.2001 doi: 10.1523/JNEUROSCI.21-05-01710.2001
|
| [25] |
L. Chariker, R. Shapley, L. S. Young, Orientation selectivity from very sparse LGN inputs in a comprehensive model of macaque V1 cortex, J. Neurosci., 36 (2016), 12368–12384. https://doi.org/10.1523/JNEUROSCI.2603-16.2016 doi: 10.1523/JNEUROSCI.2603-16.2016
|
| [26] |
L. Chariker, R. Shapley, L. S. Young, Contrast response in a comprehensive network model of macaque V1, J. Vis., 20 (2020), 16. https://doi.org/10.1167/jov.20.4.16 doi: 10.1167/jov.20.4.16
|
| [27] |
L. Campagnola, S. C. Seeman, T. Chartrand, L. Kim, A. Hoggarth, C. Gamlin, et al., Local connectivity and synaptic dynamics in mouse and human neocortex, Science, 375 (2022), eabj5861. https://doi.org/10.1126/science.abj5861 doi: 10.1126/science.abj5861
|
| [28] |
A. Stepanyants, J. A. Hirsch, L. M. Martinez, Z. F. Kisvárday, A. S. Ferecskó, D. B. Chklovskii, Local potential connectivity in cat primary visual cortex, Cereb. Cortex, 18 (2008), 13–28. https://doi.org/10.1093/cercor/bhm027 doi: 10.1093/cercor/bhm027
|
| [29] |
T. A. Hage, A. Bosma-Moody, C. A. Baker, M. B. Kratz, L. Campagnola, T. Jarsky, et al., Synaptic connectivity to L2/3 of primary visual cortex measured by two-photon optogenetic stimulation, eLife, 11 (2022), e71103. https://doi.org/10.7554/eLife.71103 doi: 10.7554/eLife.71103
|
| [30] |
T. W. Troyer, A. E. Krukowski, N. J. Priebe, K. D. Miller, Contrast-invariant orientation tuning in cat visual cortex: thalamocortical input tuning and correlation-based intracortical connectivity, J. Neurosci., 18 (1998), 5908–5927. https://doi.org/10.1523/JNEUROSCI.18-15-05908.1998 doi: 10.1523/JNEUROSCI.18-15-05908.1998
|
| [31] |
J. Kremkow, L. U. Perrinet, C. Monier, J. M. Alonso, A. Aertsen, Y. Frégnac, et al., Push-pull receptive field organization and synaptic depression: mechanisms for reliably encoding naturalistic stimuli in V1, Front. Neural Circuits, 10 (2016), 37. https://doi.org/10.3389/fncir.2016.00037 doi: 10.3389/fncir.2016.00037
|
| [32] |
M. M. Taylor, D. Contreras, A. Destexhe, Y. Frégnac, J. Antolik, An anatomically constrained model of V1 simple cells predicts the coexistence of push-pull and broad inhibition, J. Neurosci., 41 (2021), 7797–7812. https://doi.org/10.1523/JNEUROSCI.0928-20.2021 doi: 10.1523/JNEUROSCI.0928-20.2021
|
| [33] |
P. Buzás, K. Kovács, A. S. Ferecskó, J. M. Budd, U. T. Eysel, Z. F. Kisvárday, Model-based analysis of excitatory lateral connections in the visual cortex, J. Comp. Neurol., 499 (2006), 861–881. https://doi.org/10.1002/cne.21134 doi: 10.1002/cne.21134
|
| [34] |
H. Ko, S. Hofer, B. Pichler, K. A. Buchanan, P. J. Sjöström, T. D. Mrsic-Flogel, Functional specificity of local synaptic connections in neocortical networks, Nature, 473 (2011), 87–91. https://doi.org/10.1038/nature09880 doi: 10.1038/nature09880
|
| [35] |
N. Voges, A. Schüz, A. Aertsen, S. Rotter, A modeler's view on the spatial structure of intrinsic horizontal connectivity in the neocortex, Prog. Neurobiol., 92 (2010), 277–292. https://doi.org/10.1016/j.pneurobio.2010.05.001 doi: 10.1016/j.pneurobio.2010.05.001
|
| [36] |
T. Binzegger, R. J. Douglas, K. A. Martin, A quantitative map of the circuit of cat primary visual cortex, J. Neurosci., 24 (2004), 8441–8453. https://doi.org/10.1523/JNEUROSCI.1400-04.2004 doi: 10.1523/JNEUROSCI.1400-04.2004
|
| [37] |
W. Li, V. Piëch, C. D. Gilbert, Contour saliency in primary visual cortex, Neuron, 50 (2006), 951–962. https://doi.org/10.1016/j.neuron.2006.04.035 doi: 10.1016/j.neuron.2006.04.035
|
| [38] | H. You, G. Indiveri, D. R. Muir, Surround suppression explained by long-range recruitment of local competition, in a columnar V1 model, preprint, arXiv: 1611.00945. https://doi.org/10.48550/arXiv.1611.00945 |
| [39] |
A. M. M. Oswald, B. Doiron, J. Rinzel, A. D. Reyes, Spatial profile and differential recruitment of GABAB modulate oscillatory activity in auditory cortex, J. Neurosci., 29 (2009), 10321–10334. https://doi.org/10.1523/JNEUROSCI.1703-09.2009 doi: 10.1523/JNEUROSCI.1703-09.2009
|
| [40] |
O. W. Layton, E. Mingolla, A. Yazdanbakhsh, Neural dynamics of feedforward and feedback processing in figure-ground segregation, Front. Psychol., 5 (2014), 972. https://doi.org/10.3389/fpsyg.2014.00972 doi: 10.3389/fpsyg.2014.00972
|
| [41] |
O. W. Layton, E. Mingolla, A. Yazdanbakhsh, Dynamic coding of border-ownership in visual cortex, J. Vis., 12 (2012), 8. https://doi.org/10.1167/12.13.8 doi: 10.1167/12.13.8
|
| [42] |
O. W. Layton, A. Yazdanbakhsh, A neural model of border-ownership from kinetic occlusion, Vision Res., 106 (2015), 64–80. https://doi.org/10.1016/j.visres.2014.11.002 doi: 10.1016/j.visres.2014.11.002
|
| [43] |
E. Gao, Y. Yang, X. Sun, Dynamical regulation of hippocampal CA3 ripple oscillations by athorny pyramidal neurons, Int. J. Dyn. Control, 13 (2025), 358. https://doi.org/10.1007/s40435-025-01865-6 doi: 10.1007/s40435-025-01865-6
|
| [44] |
H. Markram, M. Tsodyks, Redistribution of synaptic efficacy between neocortical pyramidal neurons, Nature, 382 (1996), 807–810. https://doi.org/10.1038/382807a0 doi: 10.1038/382807a0
|
| [45] |
V. Chokshi, B. D. Grier, A. Dykman, C. L. Lantz, E. Niebur, E. M. Quinlan, et al., Naturalistic spike trains drive state-dependent homeostatic plasticity in superficial layers of visual cortex, Front. Synaptic Neurosci., 13 (2021), 663282. https://doi.org/10.3389/fnsyn.2021.663282 doi: 10.3389/fnsyn.2021.663282
|
| [46] |
M. Sarter, B. Givens, J. P. Bruno, The cognitive neuroscience of sustained attention: where top-down meets bottom-up, Brain Res. Rev., 35 (2001), 146–160. https://doi.org/10.1016/s0165-0173(01)00044-3 doi: 10.1016/s0165-0173(01)00044-3
|
| [47] |
J. Poort, M. W. Self, B. van Vugt, H. Malkki, P. R. Roelfsema, Texture segregation causes early figure enhancement and later ground suppression in areas V1 and V4 of visual cortex, Cereb. Cortex, 26 (2016), 3964–3976. https://doi.org/10.1093/cercor/bhw235 doi: 10.1093/cercor/bhw235
|
| [48] |
J. J. Knierim, D. C. van Essen, Neuronal responses to static texture patterns in area V1 of the alert macaque monkey, J. Neurophysiol., 67 (1992), 961–980. https://doi.org/10.1152/jn.1992.67.4.961 doi: 10.1152/jn.1992.67.4.961
|
| [49] |
T. Wachtler, T. J. Sejnowski, T. D. Albright, Representation of color stimuli in awake macaque primary visual cortex, Neuron, 37 (2003), 681–691. https://doi.org/10.1016/s0896-6273(03)00035-7 doi: 10.1016/s0896-6273(03)00035-7
|
| [50] |
Y. S. Hwang, C. Maclachlan, J. Blanc, A. Dubois, C. C. H. Petersen, G. Knott, et al., 3D ultrastructure of synaptic inputs to distinct GABAergic neurons in the mouse primary visual cortex, Cereb. Cortex, 31 (2021), 2610–2624. https://doi.org/10.1093/cercor/bhaa378 doi: 10.1093/cercor/bhaa378
|
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Peihan Wang, Fang Han, Hao Yang. Effect of long-range connections on surface inhibition in V1 area based on large-scale neural network modeling[J]. Electronic Research Archive, 2025, 33(12): 7999-8018. doi: 10.3934/era.2025352
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