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Visual stimuli modulate precise synchronous firing within the thalamus

Published online by Cambridge University Press:  04 January 2008

Jose-Manuel Alonso ,
Chun-I Yeh  and
Carl R. Stoelzel
Jose-Manuel Alonso*
Affiliation:
Department of Biological Sciences, SUNY-Optometry, New York, USA
Chun-I Yeh
Affiliation:
Department of Biological Sciences, SUNY-Optometry, New York, USA Department of Psychology, University of Connecticut, Storrs, Connecticut, USA
Carl R. Stoelzel
Affiliation:
Department of Psychology, University of Connecticut, Storrs, Connecticut, USA
*
Correspondence should be addressed to: Jose-Manuel Alonso, State University of New York, State College of Optometry, 33 West, 42nd street, 17th floor, New York, NY 10036, USA phone: 212-938-5573 fax: 212-938-5796 email: [email protected]
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Abstract

The work of Mircea Steriade demonstrated that the neocortex could synchronize large regions of the thalamus within 10−100 msec. Unlike the synchrony generated by the cortex, the retinal afferents synchronize a restricted group of neighboring thalamic neurons with <1-msec precision. Here, we use a large sample (n = 372) of simultaneous recordings from neighboring neurons in the lateral geniculate nucleus (LGN) to illustrate the high specificity of the synchrony generated by retinal afferents and its dependency on sensory stimulation. First, we demonstrate that cells sharing a retinal afferent show a balanced receptive field diversity: although slight receptive field mismatches are common, the largest mismatches in a specific property (e.g. receptive field size) are restricted to cells that are precisely matched in other properties (e.g. receptive field overlap). Second, we show that these receptive field mismatches are functionally important and can lead to a 5-fold variation in the percentage of synchronous spikes driven by the shared retinal afferent under different stimulus conditions. Based on these and other findings, we speculate that the precise synchronous firing of cells sharing a retinal afferent might serve to amplify local stimuli that might be too brief and small to generate a large number of thalamic spikes.

Keywords

LGNretinogeniculatecorrelated firingvisual cortexspike timingthalamocortical
Type
Original Articles
Information
Thalamus & Related Systems , Volume 4 , Issue 1 , March 2008 , pp. 21 - 34
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Abeles, M. and Gerstein, G.L. (1988) Detecting spatiotemporal firing patterns among simultaneously recorded single neurons. Journal of Neurophysiology 60, 909–924.CrossRefGoogle ScholarPubMed
Alonso, J.M. (2006) Neuroscience. Neurons find strength through synchrony in the brain. Science 312, 1604–1605.CrossRefGoogle ScholarPubMed
Alonso, J.M., Usrey, W.M. and Reid, R.C. (1996) Precisely correlated firing in cells of the lateral geniculate nucleus. Nature 383, 815–819.CrossRefGoogle ScholarPubMed
Alonso, J.M., Usrey, W.M. and Reid, R.C. (2001) Rules of connectivity between geniculate cells and simple cells in cat primary visual cortex. Journal of Neuroscience 21, 4002–4015.CrossRefGoogle ScholarPubMed
Alonso, J.M., Yeh, C.I., Weng, C. and Stoelzel, C.R. (2006) Retinogeniculate connections: a balancing act between connection specificity and receptive field diversity. Progress in Brain Research 154, 3–13.CrossRefGoogle ScholarPubMed
Brivanlou, I.H., Warland, D.K. and Meister, M. (1998) Mechanisms of concerted firing among retinal ganglion cells. Neuron 20, 527–539.CrossRefGoogle ScholarPubMed
Brody, C.D. (1998) Slow covariations in neuronal resting potentials can lead to artefactually fast cross-correlations in their spike trains. Journal of Neurophysiology 80, 3345–3351.CrossRefGoogle ScholarPubMed
Bruno, R.M. and Sakmann, B. (2006) Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312, 1622–1627.CrossRefGoogle ScholarPubMed
Castro-Alamancos, M.A. (2002) Different temporal processing of sensory inputs in the rat thalamus during quiescent and information processing states in vivo. Journal of Physiology 539, 567–578.CrossRefGoogle ScholarPubMed
Chen, C. and Regehr, W.G. (2000) Developmental remodeling of the retinogeniculate synapse. Neuron 28, 955–966.CrossRefGoogle ScholarPubMed
Chung, S., Li, X. and Nelson, S.B. (2002) Short-term depression at thalamocortical synapses contributes to rapid adaptation of cortical sensory responses in vivo. Neuron 34, 437–446.CrossRefGoogle ScholarPubMed
Cleland, B.G., Dubin, M.W. and Levick, W.R. (1971) Simultaneous recording of input and output of lateral geniculate neurones. Nature: New Biology 231, 191–192.Google ScholarPubMed
Cleland, B.G. and Lee, B.B. (1985) A comparison of visual responses of cat lateral geniculate nucleus neurones with those of ganglion cells afferent to them. Journal of Physiology 369, 249–268.CrossRefGoogle Scholar
Dan, Y., Atick, J.J. and Reid, R.C. (1996) Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory. Journal of Neuroscience 16, 3351–3362.CrossRefGoogle ScholarPubMed
Eckhorn, R. and Thomas, U. (1993) A new method for the insertion of multiple microprobes into neural and muscular tissue, including fiber electrodes, fine wires, needles and microsensors. Journal of Neuroscience Methods 49, 175–179.CrossRefGoogle ScholarPubMed
Gray, C.M. (1999) The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron 24, 31–47, 111–125.CrossRefGoogle ScholarPubMed
Hamos, J.E., Van Horn, S.C., Raczkowski, D. and Sherman, S.M. (1987) Synaptic circuits involving an individual retinogeniculate axon in the cat. Journal of Comparative Neurology 259, 165–192.CrossRefGoogle ScholarPubMed
Hecht, S., Shlaer, S. and Pirenne, M.H. (1942) Energy, quanta, and vision. Journal of General Physiology 25, 819–840.CrossRefGoogle ScholarPubMed
Hirsch, J.A., Alonso, J.M., Reid, R.C. and Martinez, L.M. (1998) Synaptic integration in striate cortical simple cells. Journal of Neuroscience 18, 9517–9528.CrossRefGoogle ScholarPubMed
Hirsch, J.A., Martinez, L.M., Alonso, J.M., Desai, K., Pillai, C. and Pierre, C. (2002) Synaptic physiology of the flow of information in the cat's visual cortex in vivo. Journal of Physiology 540, 335–350.CrossRefGoogle ScholarPubMed
Hochstein, S. and Shapley, R.M. (1976) Quantitative analysis of retinal ganglion cell classifications. Journal of Physiology 262, 237–264.CrossRefGoogle ScholarPubMed
Jones, J.P. and Palmer, L.A. (1987) The two-dimensional spatial structure of simple receptive fields in cat striate cortex. Journal of Neurophysiology 58, 1187–1211.CrossRefGoogle ScholarPubMed
Kara, P. and Reid, R.C. (1999) Efficacy of disynaptic retinal inputs to visual cortex. Society for Neuroscience Abstracts 25, 277.Google Scholar
Lampl, I., Anderson, J.S., Gillespie, D.C. and Ferster, D. (2001) Prediction of orientation selectivity from receptive field architecture in simple cells of cat visual cortex. Neuron 30, 263–274.CrossRefGoogle ScholarPubMed
Lee, B.B., Cleland, B.G. and Creutzfeldt, O.D. (1977) The retinal input to cells in area 17 of the cat's cortex. Experimental Brain Research 30, 527–538.CrossRefGoogle ScholarPubMed
Mastronarde, D.N. (1987) Two classes of single-input X-cells in cat lateral geniculate nucleus. II. Retinal inputs and the generation of receptive-field properties. Journal of Neurophysiology 57, 381–413.CrossRefGoogle ScholarPubMed
Mastronarde, D.N. (1989) Correlated firing of retinal ganglion cells. Trends in Neurosciences 12, 75–80.CrossRefGoogle ScholarPubMed
Mastronarde, D.N. (1992) Nonlagged relay cells and interneurons in the cat lateral geniculate nucleus: receptive-field properties and retinal inputs. Visual Neuroscience 8, 407–441.CrossRefGoogle ScholarPubMed
Orban, G.A., Hoffmann, K.P. and Duysens, J. (1985) Velocity selectivity in the cat visual system. I. Responses of LGN cells to moving bar stimuli: a comparison with cortical areas 17 and 18. Journal of Neurophysiology 54, 1026–1049.CrossRefGoogle Scholar
Perkel, D.H., Gerstein, G.L. and Moore, G.P. (1967) Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophysical Journal 7, 419–440.CrossRefGoogle ScholarPubMed
Reich, D.S., Mechler, F. and Victor, J.D. (2001) Independent and redundant information in nearby cortical neurons. Science 294, 2566–8.CrossRefGoogle ScholarPubMed
Reid, R.C., Victor, J.D. and Shapley, R.M. (1997) The use of m-sequences in the analysis of visual neurons: linear receptive field properties. Visual Neuroscience 14, 1015–1027.CrossRefGoogle ScholarPubMed
Rieke, F., Warland, D., de Ruyter van Steveninck, R.R. and Bialek, W. (1997) Spikes: exploring the neural code. (2nd ed) MIT Press.Google Scholar
Ringach, D.L. (2004) Haphazard wiring of simple receptive fields and orientation columns in visual cortex. Journal of Neurophysiology 92, 468–476.CrossRefGoogle ScholarPubMed
Schnitzer, M.J. and Meister, M. (2003) Multineuronal firing patterns in the signal from eye to brain. Neuron 37, 499–511.CrossRefGoogle ScholarPubMed
Singer, W. (1999) Neuronal synchrony: a versatile code for the definition of relations? Neuron 24, 49–65, 111–125.CrossRefGoogle ScholarPubMed
Steriade, M. (2005) Sleep, epilepsy and thalamic reticular inhibitory neurons. Trends in Neurosciences 28, 317–324.CrossRefGoogle ScholarPubMed
Steriade, M. and Timofeev, I. (2003) Neuronal plasticity in thalamocortical networks during sleep and waking oscillations. Neuron 37, 563–576.CrossRefGoogle ScholarPubMed
Sterling, P. (2004) How retinal circuits optimize the transfer of visual information. In Chalupa, L.M. and Werner, J.S. (eds) The Visual Neurosciences. MIT Press, pp. 234–259.Google Scholar
Sutter, E.E. (1987) A practical non-stochastic approach to nonlinear time-domain analysis. In Marmarelis, V.Z. (ed.) Advanced Methods in Physiological System Modeling. Vol. 1. University of Southern California, Los Angeles.Google Scholar
Sutter, E.E. (1991) A deterministic approach to nonlinear systems analysis. In Pinter, R. and Nabet, B. (eds) Nonlinear Vision. CRC Press, pp. 171–220.Google Scholar
Swadlow, H.A. and Gusev, A.G. (2001) The impact of ‘bursting’ thalamic impulses at a neocortical synapse. Nature Neuroscience 4, 402–408.CrossRefGoogle Scholar
Swadlow, H.A. and Gusev, A.G. (2002) Receptive-field construction in cortical inhibitory interneurons. Nature Neuroscience 5, 403–404.CrossRefGoogle ScholarPubMed
Usrey, W.M., Reppas, J.B. and Reid, R.C. (1998) Paired-spike interactions and synaptic efficacy of retinal inputs to the thalamus. Nature 395, 384–387.CrossRefGoogle Scholar
Usrey, W.M., Reppas, J.B. and Reid, R.C. (1999) Specificity and strength of retinogeniculate connections. Journal of Neurophysiology 82, 3527–3540.CrossRefGoogle ScholarPubMed
Yeh, C.I., Stoelzel, C.R. and Alonso, J.M. (2003) Two different types of Y cells in the cat lateral geniculate nucleus. Journal of Neurophysiology 90, 1852–1864.CrossRefGoogle ScholarPubMed