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A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level

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Abstract

Visual attention is a selective process of visual information and improves perceptual performance by modulating activities of neurons in the visual system. It has been reported that attention increased firing rates of neurons, reduced their response variability and improved reliability of coding relevant stimuli. Recent neurophysiological studies demonstrated that attention also enhanced the synaptic efficacy between neurons mediated through NMDA and AMPA receptors. Majority of computational models of attention usually are based on firing rates, which cannot explain attentional modulations observed at the synaptic level. To understand mechanisms of attentional modulations at the synaptic level, we proposed a neural network consisting of three layers, corresponding to three different brain regions. Each layer has excitatory and inhibitory neurons. Each neuron was modeled by the Hodgkin–Huxley model. The connections between neurons were through excitatory AMPA and NMDA receptors, as well as inhibitory GABAA receptors. Since the binding process of neurotransmitters with receptors is stochastic in the synapse, it is hypothesized that attention could reduce the variation of the stochastic binding process and increase the fraction of bound receptors in the model. We investigated how attention modulated neurons’ responses at the synaptic level on the basis of this hypothesis. Simulated results demonstrated that attention increased firing rates of neurons and reduced their response variability. The attention-induced effects were stronger in higher regions compared to those in lower regions, and stronger for inhibitory neurons than for excitatory neurons. In addition, AMPA receptor antagonist (CNQX) impaired attention-induced modulations on neurons’ responses, while NMDA receptor antagonist (APV) did not. These results suggest that attention may modulate neuronal activity at the synaptic level.

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References

  • Anderson EB, Mitchell JF, Reynolds JH (2013) Attention-dependent reductions in burstiness and action potential height in macaque area V4. Nat Neurosci 16(8):1125–1131

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Antonerxleben K, Carrasco M (2013) Attentional enhancement of spatial resolution: linking behavioural and neurophysiological evidence. Nat Rev Neurosci 14(3):188–200

    Article  CAS  Google Scholar 

  • Ardid S, Wang XJ, Compte A (2007) An integrated microcircuit model of attentional processing in the neocortex. J Neurosci 27(32):8486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ardid S, Wang XJ, Gomezcabrero D, Compte A (2010) Reconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas. J Neurosci 30(8):2856

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ardid S, Vinck M, Kaping D, Marquez S, Everling S, Womelsdorf T (2015) Mapping of functionally characterized cell classes onto canonical circuit operations in primate prefrontal cortex. J Neurosci 35(7):2975–2991

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (2004) Potassium model for slow (2-3 Hz) in vivo neocortical paroxysmal oscillations. J Neurophysiol 92(2):1116–1132

    Article  CAS  PubMed  Google Scholar 

  • Beuth F, Hamker FH (2015) A mechanistic cortical microcircuit of attention for amplification, normalization and suppression. Vision Res 116(12):241–257

    Article  PubMed  Google Scholar 

  • Boynton GM (2009) A framework for describing the effects of attention on visual responses. Vision Res 49(10):1129–1143

    Article  PubMed  Google Scholar 

  • Briggs F, Mangun GR, Usrey WM (2013) Attention enhances synaptic efficacy and the signal-to-noise ratio in neural circuits. Nature 499(7459):476–480. https://doi.org/10.1038/nature12276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Buehlmann A, Deco G (2008) The neuronal basis of attention: rate versus synchronization modulation. J Neurosci 28(30):7679–7686

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Buia C, Tiesinga P (2006) Attentional modulation of firing rate and synchrony in a model cortical network. J Comput Neurosci 20(3):247–264

    Article  PubMed  Google Scholar 

  • Buia CI, Tiesinga PH (2008) Role of interneuron diversity in the cortical microcircuit for attention. J Neurophysiol 99(5):2158–2182

    Article  PubMed  Google Scholar 

  • Carrasco M (2011) Visual attention: the past 25 years. Vision Res 51(13):1484–1525

    Article  PubMed  PubMed Central  Google Scholar 

  • Carrasco M, Ling S, Read S (2004) Attention alters appearance. Nat Neurosci 7(3):308–313

    Article  CAS  PubMed  Google Scholar 

  • Deco G, Lee TS (2015) The role of early visual cortex in visual integration: a neural model of recurrent interaction. Eur J Neurosci 20(4):1089–1100

    Article  Google Scholar 

  • Deco G, Thiele A (2011) Cholinergic control of cortical network interactions enables feedback-mediated attentional modulation. Eur J Neurosci 34(1):146–157

    Article  PubMed  Google Scholar 

  • Destexhe A, Paré D (1999) Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. J Neurophysiol 81(4):1531–1547

    Article  CAS  PubMed  Google Scholar 

  • Destexhe A, Rudolph M, Fellous JM, Sejnowski TJ (2001) Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107(1):13–24

    Article  CAS  PubMed  Google Scholar 

  • Destexhe A, Mainen Z, Sejnowski T (2008) An efficient method for computing synaptic conductances based on a kinetic model of receptor binding. Neural Comput 6(1):14–18

    Article  Google Scholar 

  • Di Maio V, Ventriglia F, Santillo S (2017) Stochastic, structural and functional factors influencing AMPA and NMDA synaptic response variability: a review. Neuronal Signaling. https://doi.org/10.1042/NS20160051

    Article  PubMed  PubMed Central  Google Scholar 

  • Dobrunz LE, Stevens CF (1997) Heterogeneity of release probability, facilitation, and depletion at central synapses. Neuron 18(6):995–1008

    Article  CAS  PubMed  Google Scholar 

  • Fan H, Pan X, Wang R, Sakagami M (2017) Differences in reward processing between putative cell types in primate prefrontal cortex. PLoS ONE 12(12):e0189771

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gardner JL (2015) A case for human systems neuroscience. Neuroscience 296:130–137

    Article  CAS  PubMed  Google Scholar 

  • Gazzaniga EBMS (2004) The cognitive neurosciences, 3rd edn. MIT Press, Cambridge

    Google Scholar 

  • Gibb AJ (1978) Neurotransmitter receptor binding. Raven*

  • Gratton C, Yousef S, Aarts E, Wallace DL, D’Esposito M, Silver MA (2017) Cholinergic, but not dopaminergic or noradrenergic, enhancement sharpens visual spatial perception in humans. The Journal of Neuroscience 37(16):4405–4415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gravier A, Quek C, Duch W, Wahab A, Gravier-Rymaszewska J (2016) Neural network modelling of the influence of channelopathies on reflex visual attention. Cogn Neurodyn 10(1):49–72. https://doi.org/10.1007/s11571-015-9365-x

    Article  PubMed  Google Scholar 

  • Guo DQ, Wang QY, Perc M (2012) Complex synchronous behavior in interneuronal networks with delayed inhibitory and fast electrical synapses. Phys Rev E 85(6):061905

    Article  CAS  Google Scholar 

  • Guo DQ, Chen MM, Perc M, Wu SD, Xia C, Zhang YS et al (2016a) Firing regulation of fast-spiking interneurons by autaptic inhibition. EPL 114(3):30001

    Article  CAS  Google Scholar 

  • Guo DQ, Wu SD, Chen MM, Perc M, Zhang YS, Ma JL et al (2016b) Regulation of irregular neuronal firing by autaptic transmission. Scientific Reports 6:26096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Haab L, Trenado C, Strauss DJ (2009) Modeling the influence of the hippocampal comparator function on selective attention according to stimulus–novelty. Springer, Berlin Heidelberg

    Book  Google Scholar 

  • Haab L, Trenado C, Mai M, Strauss DJJCN (2011) Neurofunctional model of large-scale correlates of selective attention governed by stimulus-novelty. Cogn Neurodyn 5(1):103–111

    Article  PubMed  PubMed Central  Google Scholar 

  • Herrero JL, Roberts MJ, Delicato LS, Gieselmann MA, Dayan P, Thiele A (2008) Acetylcholine contributes through muscarinic receptors to attentional modulation in V1. Nature 454(7208):1110–1114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Herrero JL, Gieselmann MA, Sanayei M, Thiele A (2013) Attention-induced variance and noise correlation reduction in macaque V1 is mediated by NMDA receptors. Neuron 78(4):729–739

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hodgkin AL, Huxley AF (1989) A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol 52(1–2):25–71

    Google Scholar 

  • Ison MJ, Mormann F, Cerf M, Koch C, Fried I, Quiroga RQ (2011) Selectivity of pyramidal cells and interneurons in the human medial temporal lobe. J Neurophysiol 106(4):1713–1721

    Article  PubMed  PubMed Central  Google Scholar 

  • Itti L, Koch C (2000) A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Res 40(10):1489–1506

    Article  CAS  PubMed  Google Scholar 

  • Kanashiro T, Ocker GK, Cohen MR, Doiron B (2017) Attentional modulation of neuronal variability in circuit models of cortex. Elife. https://doi.org/10.7554/eLife.23978

    Article  PubMed  PubMed Central  Google Scholar 

  • Klinkenberg I, Sambeth A, Blokland A (2011) Acetylcholine and attention. Behav Brain Res 221:430–442

    Article  CAS  PubMed  Google Scholar 

  • Koch C (1989) Methods in neuronal modeling: from synapses to networks. MIT Press, Cambridge

    Google Scholar 

  • Lanyon LJ, Denham SLJCN (2009) Modelling attention in individual cells leads to a system with realistic saccade behaviours. Cognit Neurodyn 3(3):223–242

    Article  Google Scholar 

  • Lee J, Maunsell JH (2010) Attentional modulation of MT neurons with single or multiple stimuli in their receptive fields. J Neurosci 30(8):3058–3066

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mitchell JF, Sundberg KA, Reynolds JH (2007) Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron 55(1):131–141

    Article  CAS  PubMed  Google Scholar 

  • Parhizi B, Daliri MR, Behroozi M (2018) Decoding the different states of visual attention using functional and effective connectivity features in fMRI data. Cogn Neurodyn 12(2):157–170. https://doi.org/10.1007/s11571-017-9461-1

    Article  PubMed  Google Scholar 

  • Phillips MA, Constantine-Paton M (2009) NMDA receptors and development. Encyclopedia of Neuroscience, 1165–1175

  • Picciotto MR, Higley MJ, Mineur YS (2012) Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior. Neuron 76:116–129

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Posner MI, Petersen SE (2012) The attention system of the human brain. Annu Rev Neurosci 13(1):25–42

    Article  Google Scholar 

  • Pospischil M, Toledo-Rodriguez M, Monier C, Piwkowska Z, Bal T, Frégnac Y et al (2008) Minimal hodgkin–huxley type models for different classes of cortical and thalamic neurons. Biol Cybern 99(4–5):427–441

    Article  PubMed  Google Scholar 

  • Reynolds JH, Heeger DJ (2009) The normalization model of attention. Neuron 61(2):168–185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Reynolds JH, Chelazzi L, Desimone R (1999) Competitive mechanisms subserve attention in macaque areas V2 and V4. J Neurosci 19(5):1736–1753

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sommer MA (2007) Microcircuits for attention. Neuron 55(1):6–8

    Article  CAS  PubMed  Google Scholar 

  • Sprague CT, Saproo S, Serences JT (2015) Visual attention mitigates information loss in small- and large-scale neural codes. Trends in Cognitive Sciences 19(4):215–226

    Article  PubMed  PubMed Central  Google Scholar 

  • Thiele A, Bellgrove MA (2018) Neuromodulation of attention. Neuron 97:769–785

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Thiele A, Brandt C, Dasilva M, Gotthardt S, Chicharro D, Panzeri S et al (2016) Attention induced gain stabilization in broad and narrow-spiking cells in the frontal eye-field of macaque monkeys. J Neurosci 36(29):7601–7612

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Treue S, Maunsell JH (2005) Effects of attention on the processing of motion in macaque middle temporal and medial superior temporal visual cortical areas. J Neurosci 19(17):7591–7602

    Article  Google Scholar 

  • Ueda M, Shibata T (2007) Stochastic signal processing and transduction in chemotactic response of eukaryotic cells. Biophys J 93(1):11–20. https://doi.org/10.1529/biophysj.106.100263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Varela JA, Dupuis JP, Etchepare L, Espana A, Cognet L, Groc L (2016) Targeting neurotransmitter receptors with nanoparticles in vivo allows single-molecule tracking in acute brain slices. Nat Commun 7:10947. https://doi.org/10.1038/ncomms10947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wagatsuma N, Potjans TC, Diesmann M, Sakai K, Fukai T (2013) Spatial and feature-based attention in a layered cortical microcircuit model. PLoS ONE 8(12):e80788

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yang H, Xu-Friedman MA (2013) Stochastic properties of neurotransmitter release expand the dynamic range of synapses. J Neurosci 33(36):14406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang HH, Wang QY, Perc M, Chen GR (2013) Synaptic plasticity induced transition of spike propagation in neuronal networks. Commun Nonlinear Sci Numer Simul 18(3):601–615

    Article  Google Scholar 

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Funding

This study was funded by National Natural Science Foundation of China (Nos. 11232005, 11472104, 11702096, 11872180) and sponsored by Shanghai Pujiang Program (No. 13PJ1402000).

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Correspondence to Xiaochuan Pan.

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Zhang, T., Pan, X., Xu, X. et al. A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level. Cogn Neurodyn 13, 579–599 (2019). https://doi.org/10.1007/s11571-019-09540-1

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