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Connectional parameters determine multisensory processing in a spiking network model of multisensory convergence

Experimental Brain Research volume 213, Article number: 329 (2011) Cite this article

Abstract

For the brain to synthesize information from different sensory modalities, connections from different sensory systems must converge onto individual neurons. However, despite being the definitive, first step in the multisensory process, little is known about multisensory convergence at the neuronal level. This lack of knowledge may be due to the difficulty for biological experiments to manipulate and test the connectional parameters that define convergence. Therefore, the present study used a computational network of spiking neurons to measure the influence of convergence from two separate projection areas on the responses of neurons in a convergent area. Systematic changes in the proportion of extrinsic projections, the proportion of intrinsic connections, or the amount of local inhibitory contacts affected the multisensory properties of neurons in the convergent area by influencing (1) the proportion of multisensory neurons generated, (2) the proportion of neurons that generate integrated multisensory responses, and (3) the magnitude of multisensory integration. These simulations provide insight into the connectional parameters of convergence that contribute to the generation of populations of multisensory neurons in different neural regions as well as indicate that the simple effect of multisensory convergence is sufficient to generate multisensory properties like those of biological multisensory neurons.

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References

  • Allman BL, Keniston LP, Meredith MA (2009) Not just for bimodal neurons anymore: the contribution of unimodal neurons to cortical multisensory processing. Brain Topogr 21:157–167

    PubMed  Article  Google Scholar 

  • Anastasio TJ, Patton PE (2003) A two-stage unsupervised learning algorithm reproduces multisensory enhancement in a neural network model of the corticotectal system. J Neurosci 23:6713–6727

    PubMed  CAS  Google Scholar 

  • Anastasio TJ, Patton PE, Belkacem-Boussaid K (2002) Using Bayes’ rule to model multisensory enhancement in the superior colliculus. Neural Comput 12(5):1165–1187

    Article  Google Scholar 

  • Avillac M, Hamed SB, Duhamel J-R (2007) Multisensory integration in ventral intraparietal area of the macaque monkey. J Neurosci 27:1922–1932

    PubMed  Article  CAS  Google Scholar 

  • Barraclough NE, Xiao D, Baker CI, Oram MW, Perret DI (2005) Integration of visual and auditory information by superior temporal sulcus neurons responsive to the sight of actions. J Cognit Neurosci 17:377–391

    Article  Google Scholar 

  • Bell AH, Corneil BD, Meredith MA, Munoz DP (2001) The influence of stimulus properties on multisensory processing in the awake primate superior colliculus. Can J Exp Psychol 55:125–134

    Google Scholar 

  • Bower JM, Beeman D (1998) The book of GENESIS: exploring realistic neural models with the GEneral NEural simulation system. Springer-Verlag, New York

    Google Scholar 

  • Breveglieri R, Galletti C, Monaco S, Fattori P (2008) Visual, somatosensory and bimodal activities in the macaque parietal area PEc. Cereb Cortex 18:806–816

    PubMed  Article  Google Scholar 

  • Calvert GA (2001) Crossmodal processing in the human brain: insights from functional neuroimaging studies. Cereb Cortex 11:1110–1123

    PubMed  Article  CAS  Google Scholar 

  • Carnevale NT, Hines ML (2006) The NEURON book. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Clemo HR, Allman BL, Donlan MA, Meredith MA (2007) Sensory and multisensory representations within the cat rostral suprasylvian cortices. J Comp Neurol 503:110–127

    PubMed  Article  Google Scholar 

  • Colonius H, Diederich A (2001) A maximum-likelihood approach to modeling multisensory enhancement. In: Dietterich TG, Becker S, Ghahramani Z (eds) NIPS. MIT Press, Cambridge, pp 181–187

    Google Scholar 

  • Colonius H, Diederich A (2004) Why aren’t all deep superior colliculus neurons multisensory? A Bayes’ ratio analysis. Cogn Affect Behav Neurosci 4:344–353

    PubMed  Article  Google Scholar 

  • Cuppini C, Ursino M, Magosso E, Rowland BA, Stein BE (2010) An emergent model of multisensory integration in superior colliculus neurons. Front Integr Neurosci 4:1–15

    Google Scholar 

  • Dahl CD, Logothetis NK, Kayser C (2009) Spatial organization of multisensory responses in temporal association cortex. J Neurosci 29:11924–11932

    PubMed  Article  CAS  Google Scholar 

  • DeFilipe J (1993) Neocortical neuronal diversity: chemical heterogeneity revealed by colocalization studies of classic neurotransmitters, neuropeptides, calcium-binding proteins, and cell surface molecules. Cereb Cortex 3:273–289

    Article  Google Scholar 

  • Dehner LR, Keniston LP, Clemo HR, Meredith MA (2004) Crossmodal circuitry between auditory and somatosensory areas of the cat anterior ectosylvian sulcal cortex: a ‘new’ inhibitory form of multisensory convergence. Cereb Cortex 14:387

    PubMed  Article  Google Scholar 

  • Diederich A, Colonius H (2004) Bimodal and trimodal multisensory enhancement: effects of stimulus onset and intensity on reaction time. Percept Psychophys 66:1388–1404

    PubMed  Article  Google Scholar 

  • Ermentrout GB, Galan RF, Urban NN (2008) Reliability, synchrony and noise. Trends Neurosci 31:428–434

    PubMed  Article  CAS  Google Scholar 

  • FitzHugh R (1961) Impulses and physiological states in theoretical models of nerve membrane. Biophysical J 1:445–466

    Article  CAS  Google Scholar 

  • Fuentes-Santamaria V, Alvarado JC, McHaffie JG, Stein BE (2009) Axon morphologies and convergence patterns of projections from different sensory-specific cortices of the anterior ectosylvian sulcus onto multisensory neurons in the cat superior colliculus. Cereb Cortex 19:2902–2915

    PubMed  Article  Google Scholar 

  • Ghazanfar AA, Schroeder CE (2006) Is neocortex essentially multisensory? Trends Cogn Sci 10:278–285

    PubMed  Article  Google Scholar 

  • Hebb DO (1949) The organization of behavior. Wiley, New York

    Google Scholar 

  • Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544

    PubMed  CAS  Google Scholar 

  • Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14:1569–1572

    PubMed  Article  CAS  Google Scholar 

  • Izhikevich EM (2010) Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, Cambridge

    Google Scholar 

  • Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamocortical systems. PNAS 105:3593–3598

    PubMed  Article  CAS  Google Scholar 

  • Kayser C, Petkov CI, Augath M, Logothetis NK (2005) Integration of touch and sound in auditory cortex. Neuron 48:373–384

    PubMed  Article  CAS  Google Scholar 

  • Kayser C, Petkov CI, Logothetis NK (2008) Visual modulation of neurons in auditory cortex. Cereb Cortex 18:1560–1574

    PubMed  Article  Google Scholar 

  • Keniston LP, Allman BA, Meredith MA, Clemo HR (2009) Somatosensory and multisensory properties of the medial bank of the ferret rostral suprasylvian sulcus. Exp Brain Res 196:239–251

    Google Scholar 

  • Keniston LP, Henderson SC, Meredith MA (2010) Neuroanatomical identification of crossmodal auditory inputs to interneurons in somatosensory cortex. Exp Brain Res 202:725–731

    PubMed  Article  Google Scholar 

  • Konorski J (1948) Conditioned reflexes and neuron organization. Cambridge University Press, Cambridge

    Google Scholar 

  • Lakatos P, Chen CM, O’Connell MN, Mills A, Schroeder CE (2007) Neuronal oscillations and multisensory interaction in primary auditory cortex. Neuron 18:279–292

    Article  Google Scholar 

  • Lim HK, Keniston LP, Shin JH, Nguyen CD, Meredith MA, Cios KJ (2010) A neuronal multisensory processing simulator. In: Proceedings of international joint conference neural networks at WCCI 2010, Barcelona, Spain. IEEE Press, New York, pp 281–287

  • MacGregor RJ (1987) Neural and brain modeling. Academic Press, San Diego

  • Magosso E, Cuppini C, Serino A, Di Pellegrino G, Ursino M (2008) A theoretical study of multisensory integration in the superior colliculus by a neural network model. Neural Netw 21:817–829

    PubMed  Article  Google Scholar 

  • Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160

    PubMed  Article  CAS  Google Scholar 

  • Martin JG, Meredith MA, Ahmad K (2009) Modeling multisensory enhancement with self-organizing maps. Front Compu Neurosci 3:8

    Google Scholar 

  • Meredith MA, Stein BE (1986) Visual, auditory, and somatosensory convergence on cells in the superior colliculus results in multisensory integration. J Neurophysiol 56:640–662

    PubMed  CAS  Google Scholar 

  • Meredith MA, Allman BL, Keniston LP, Clemo HR (2011) Are bimodal neurons the same throughout the brain? In: Wallace MT, Murray MM (eds) Frontiers in the neural bases of multisensory processing (in press)

  • Mergner T (2007) Modeling sensorimotor control of human upright stance. Prog Brain Res 165:283–297

    PubMed  Article  Google Scholar 

  • Oie KS, Kiemel T, Jeka JJ (2002) Multisensory fusion: simultaneous re-weighting of vision and touch for the control of human posture. Brain Res Cogn Brain Res 14:164–176

    PubMed  Article  Google Scholar 

  • Perrault TJ Jr, Vaughan JW, Stein BE, Wallace MT (2005) Superior colliculus neurons use distinct operational modes in the integration of multisensory stimuli. J Neurophysiol 93:2575–2586

    PubMed  Article  Google Scholar 

  • Reinoso-Suarez F, Roda JM (1985) Topographical organization of the cortical afferent connections to the cortex of the anterior ectosylvian sulcus in the cat. Exp Brain Res 59:313–324

    PubMed  Article  CAS  Google Scholar 

  • Romanski LM (2007) Representation and integration of auditory and visual stimuli in the primate ventral lateral prefrontal cortex. Cereb Cortex 17:i61–i69

    PubMed  Article  Google Scholar 

  • Rowland BA, Quessy S, Stanford TR, Stein BE (2007a) Multisensory integration shortens physiological response latencies. J Neurosci 27:5879–5884

    PubMed  Article  CAS  Google Scholar 

  • Rowland B, Stanford T, Stein BE (2007b) A Bayseian model unifies multisensory spatial localization with the physiological properties of the superior colliculus. Exp Brain Res 180:153–161

    PubMed  Article  Google Scholar 

  • Rowland BA, Stanford RT, Stein BE (2007c) A model of the neural mechanisms underlying multisensory integration in the superior colliculus. Percept 36:1341

    Article  Google Scholar 

  • Selzer B, Pandya DN (1994) Parietal, temporal, and occipital projections to cortex of the superior temporal sulcus in the rhesus monkey: a retrograde tracer study. J Comp Neurol 343:445–463

    Article  Google Scholar 

  • Shanahan M (2008) Dynamical complexity in small-world networks of spiking neurons. Phys Rev E Stat Nonlin Soft Matter Phys 78:041924

    PubMed  Article  Google Scholar 

  • Shore SE, Vass Z, Wys NL, Altschuler RA (2000) Trigeminal ganglion innervates the auditory brainstem. J Comp Neurol 419:271–285

    PubMed  Article  CAS  Google Scholar 

  • Song S, Miller KD, Abbot LF (2000) Competitive Hebbian learning through spike timing-dependent synaptic plasticity. Nat Neurosci 3:9

    Article  Google Scholar 

  • Stein BE, Meredith MA (1993) Merging of the senses. MIT Press, Cambridge

    Google Scholar 

  • Wallace MT, Carriere BN, Perrault TJ Jr, Vaughan JW, Stein BE (2006) The development of cortical multisensory integration. J Neurosci 26:11844–11849

    PubMed  Article  CAS  Google Scholar 

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Acknowledgments

This work was supported by NIH grant NS064675. The authors also thank Dr. JG Martin for his comments.

Conflict of interest

The authors declare that they have no conflict of interest.

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Affiliations

  1. Department of Computer Science, School of Engineering, Virginia Commonwealth University School of Medicine, Richmond, VA, USA

    H. K. Lim, J. H. Shin & K. J. Cios

  2. Department of Anatomy and Neurobiology, Virginia Commonwealth University School of Medicine, PO Box 908709, Richmond, VA, USA

    L. P. Keniston, B. L. Allman & M. A. Meredith

  3. IITiS Polish Academy of Sciences, Gliwice, Poland

    K. J. Cios

Authors
  1. H. K. Lim
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  2. L. P. Keniston
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  3. J. H. Shin
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  4. B. L. Allman
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  5. M. A. Meredith
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  6. K. J. Cios
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Corresponding author

Correspondence to M. A. Meredith.

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Lim, H.K., Keniston, L.P., Shin, J.H. et al. Connectional parameters determine multisensory processing in a spiking network model of multisensory convergence. Exp Brain Res 213, 329 (2011). https://doi.org/10.1007/s00221-011-2671-6

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  • DOI: https://doi.org/10.1007/s00221-011-2671-6

Keywords

  • Computation
  • Simulation
  • Multisensory integration
  • Bimodal
  • Cross-modal
  • Association cortex