Associating spontaneous with evoked activity in a neural mass model of cat visual cortex

Lists

Tools

Trong, M. N., Bojak, I. ORCID: https://orcid.org/0000-0003-1765-3502 and Knösche, T. R. (2013) Associating spontaneous with evoked activity in a neural mass model of cat visual cortex. NeuroImage, 66. pp. 80-87. ISSN 1053-8119

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.neuroimage.2012.10.024

Abstract/Summary

Spontaneous activity of the brain at rest frequently has been considered a mere backdrop to the salient activity evoked by external stimuli or tasks. However, the resting state of the brain consumes most of its energy budget, which suggests a far more important role. An intriguing hint comes from experimental observations of spontaneous activity patterns, which closely resemble those evoked by visual stimulation with oriented gratings, except that cortex appeared to cycle between different orientation maps. Moreover, patterns similar to those evoked by the behaviorally most relevant horizontal and vertical orientations occurred more often than those corresponding to oblique angles. We hypothesize that this kind of spontaneous activity develops at least to some degree autonomously, providing a dynamical reservoir of cortical states, which are then associated with visual stimuli through learning. To test this hypothesis, we use a biologically inspired neural mass model to simulate a patch of cat visual cortex. Spontaneous transitions between orientation states were induced by modest modifications of the neural connectivity, establishing a stable heteroclinic channel. Significantly, the experimentally observed greater frequency of states representing the behaviorally important horizontal and vertical orientations emerged spontaneously from these simulations. We then applied bar-shaped inputs to the model cortex and used Hebbian learning rules to modify the corresponding synaptic strengths. After unsupervised learning, different bar inputs reliably and exclusively evoked their associated orientation state; whereas in the absence of input, the model cortex resumed its spontaneous cycling. We conclude that the experimentally observed similarities between spontaneous and evoked activity in visual cortex can be explained as the outcome of a learning process that associates external stimuli with a preexisting reservoir of autonomous neural activity states. Our findings hence demonstrate how cortical connectivity can link the maintenance of spontaneous activity in the brain mechanistically to its core cognitive functions.

Item Type:	Article
Refereed:	Yes
Divisions:	Interdisciplinary Research Centres (IDRCs) > Centre for Integrative Neuroscience and Neurodynamics (CINN)
ID Code:	31455
Uncontrolled Keywords:	Spontaneous activity; Neural mass; Learning; Stable heteroclinic channels;
Publisher:	Elsevier

Altmetric

Deposit Details

References

Arieli, A., Sterkin, A., Grinvald, A., Aertsen, A., 1996. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871. Blomfield, S., 1974. Arithmetical operations performed by nerve cells. Brain Res. 69, 115–124. Blumenfeld, B., Bibitchkov, D., Tsodyks, M., 2006. Neural network model of the primary visual cortex: fromfunctional architecture to lateral connectivity and back. J. Comput. Neurosci. 20, 219–241. Bonhoeffer, T., Grinvald, A., 1993. The layout of the iso-orientation domains in area 18 of cat visual cortex—optical imaging reveals a pinwheel-like organization. J.Neurosci. 13, 4157–4180. Crair, M., Gillespie, D., Stryker,M., 1998. The role of visual experience in the development of columns in cat visual cortex. Science 279, 566–570. David, O., Friston, K.J., 2003. A neural mass model for MEG/EEG: coupling and neuronal dynamics. NeuroImage 20, 1743–1755. David, O., Kiebel, S.J., Harrison, L.M., Mattout, J., Kilner, J.M., Friston, K.J., 2006. Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage 30, 1255–1272. Dominey, P.F., 1995. Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning. Biol. Cybern. 73, 265–274. Dragoi, V., Sur, M., 2000. Dynamics properties of recurrent inhibition in primary visual cortex: contrast and orientation dependence of contextual effects. J. Neurophysiol. 83, 1019–1030. Fiser, J., Berkes, P., Orban, G., Lengyel, M., 2010. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14, 119–130. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711. Freeman, W.J., 1975. Mass Action in the Nervous System: Examination of the Neurophysiological Basis of Adaptive Behavior Through the EEG. Academic Press, New York San Francisco London. Galarreta, M., Hestrin, S., 1998. Frequency-dependent synaptic depression and the balance of excitation and inhibition in the neocortex. Nat. Neurosci. 1, 587–594. Goedecke, I., Kim, D., Bonhoeffer, T., Singer, W., 1997. Development of orientation preference maps in area 18 of kitten visual cortex. Eur. J. Neurosci. 9, 1754–1762. Grinvald, A., Hildesheim, R., 2004. VSDI: a new area in functional imaging of cortical dynamics. Nat. Rev. Neurosci. 5, 874–885. Gros, C., 2009. Cognitive computation with autonomously active neural networks: an emerging field. Cogn. Comput. 1, 77–90. Hirsch, J., Gilbert, C.D., 1991. Synaptic physiology of horizontal connections in the cat's visual cortex. J. Neurosci. 11, 1800–1809. Honey, C.J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.P., Meuli, R., Hagmann, P., 2009. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. U. S. A. 106, 2035–2040. Hubel, D.H.,Wiesel, T.N., 1974. Uniformity of monkey striate cortex—parallel relationship between field size, scatter, and magnification factor. J. Comp. Neurol. 158, 295–306. Hubel, D.H., Wiesel, T.N., 1977. Functional architecture of macaque monkey visual cortex: Proceedings of the Royal Society of London Series B-Biological Sciences, 198 (1-&). Huberman, A.D., Feller, M.B., Chapman, B., 2008. Mechanisms underlying development of visual maps and receptive fields. Annu. Rev. Neurosci. 31, 479–509. Jansen, B.H., Rit, V.G., 1995. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled columns. Biol. Cybern. 73, 357–366. Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., Arieli, A., 2003. Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956. Kiebel, S.J., Daunizeau, J., Friston, K.J., 2009a. Perception and hierarchical dynamics. Front. Neuroinform. 3, 20. Kiebel, S.J., von Kriegstein, K., Daunizeau, J., Friston, K.J., 2009b. Recognizing sequences of sequences. PLoS Comput. Biol. 5, e1000464. Kisvaday, Z., Martin, K., Freund, T., Magloczky, Z., Whitterige, D., Somogyi, P., 1986. Synaptic target of HRP-filled layer III pyramidal cells in the cat striate cortex. Exp. Brain Res. 64, 541–552. Koch, C., Poggio, T., 1985. The synaptic veto mechanism: does it underline direction and orientation selectivity in the visual cortex? In: Rose, D., Dobson, V. (Eds.), Models of the Visual Cortex. Wiley, New York, pp. 408–419. Koever, H., Bao, S., 2010. Cortical plasticity as a mechanism for storing Bayesian priors in sensory perception. PLoS One 5 (Article No.: e10497). Kohonen, T., 2001. Self-Organizing Maps. Springer . (Third, extended ed.). Komatsu, Y., Nakajima, S., Toyama, K., Fetz, E.E., 1988. Intracortical connectivity revealed by spike-triggered averaging in slice preparations of cat visual cortex. Brain Res. 442, 359–362. Loewel, S., Schmidt, K., Kim, D., Wolf, F., Hoffsuemmer, F., Singer, W., Bonhoeffer, T., 1998. The layout of orientation and ocular dominance domains in area 17 of strabismic cats. Eur. J. Neurosci. 10, 2629–2643. McCormick, D.A., 1999. Developmental neuroscience — spontaneous activity: signal or noise? Science 285, 541–543. McGuire, B., Gilbert, C., Rivlin, P., Wiesel, T., 1991. Targets of horizontal connections in macaque primary visual cortex. J. Comp. Neurol. 305, 370–392. Oja, E., 1982. Simplified neuron model as a principal component analyzer. J. Math. Biol. 15, 267–273. Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I., 2006. Dynamical principles in neuroscience. Rev. Mod. Phys. 78, 1213–1265. Rabinovich, M.I., Huerta, R., Varona, P., Afraimovich, V.S., 2008. Transient cognitive dynamics, metastability, and decision making. PLoS Comput. Biol. 4, e1000072. Roerig, B., Katz, L., 1998. Relationships of synaptic input patterns to orientation preference maps in ferret visual cortex. Soc. Neurosci. 24. Sengpiel, F., Kind, P.C., 2002. The role of activity in development of the visual system. Curr. Biol. 12, R818–R826. Sernagor, E., Grzywacz, N.M., 1996. Influence of spontaneous activity and visual experience on developing retinal receptive fields. Curr. Biol. 6, 1503–1508. Spiegler, A., Kiebel, S.J., Atay, F.M., Knösche, T.R., 2010. Bifurcation analysis of neural mass models: impact of extrinsic inputs and dendritic time constants. NeuroImage 52, 1041–1058. Sur, M., Angelucci, A., Sharma, J., 1999. Rewiring cortex: the role of patterned activity in development and plasticity of neocortical circuits. J. Neurobiol. 41, 33–43. Thomson, A., Deuchars, J., 1994. Temporal and spatial properties of local circuits in neocortex. Trends Neurosci. 17, 119–126. Thomson, A., West, D., 1993. Fluctuations in pyramid–pyramid excitatory postsynaptic potentials modified by presynaptic firing pattern and postsynaptic membrane potential using paired intracellular recordings in rat neocortex. Neuroscience 54, 329–346. Toyama, K., Kimura, M., Tanaka, K., 1981. Organization of cat visual cortex as investigated by cross-correlation technique. J. Neurophysiol. 46, 202–214. Tucker, T., Katz, L., 1998. Organization of excitatory and inhibitory connections in layer 4 of ferret visual cortex. Soc. Neurosci. 24, 1756. Weliky, M., Kandler, K., Fitzpatrick, D., Katz, L., 1995. Pattern of excitation and inhibition evoked by horizontal connections in visual cortex share a common relationship to orientation columns. Neuron 15, 541–555. Wendling, F., Bellanger, J.J., Bartolomei, F., Chauvel, P., 2000. Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol. Cybern. 83, 367–378. Wilson, H.R., Cowan, J.D., 1973. Mathematical theory of functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13, 55–80. Zetterberg, L.H., Kristiansson, L., Mossberg, K., 1978. Performance of a model for a local neuron population. Biol. Cybern. 31, 15–26. Zhang, D., Raichle, M.E., 2010. Disease and the brain's dark energy. Nat. Rev. Neurol. 6, 15–Blumenfeld, B., Bibitchkov, D., Tsodyks, M., 2006. Neural network model of the primary visual cortex: fromfunctional architecture to lateral connectivity and back. J. Comput. Neurosci. 20, 219–241. Bonhoeffer, T., Grinvald, A., 1993. The layout of the iso-orientation domains in area 18 of cat visual cortex—optical imaging reveals a pinwheel-like organization. J.Neurosci. 13, 4157–4180. Crair, M., Gillespie, D., Stryker,M., 1998. The role of visual experience in the development of columns in cat visual cortex. Science 279, 566–570. David, O., Friston, K.J., 2003. A neural mass model for MEG/EEG: coupling and neuronal dynamics. NeuroImage 20, 1743–1755. David, O., Kiebel, S.J., Harrison, L.M., Mattout, J., Kilner, J.M., Friston, K.J., 2006. Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage 30, 1255–1272. Dominey, P.F., 1995. Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning. Biol. Cybern. 73, 265–274. Dragoi, V., Sur, M., 2000. Dynamics properties of recurrent inhibition in primary visual cortex: contrast and orientation dependence of contextual effects. J. Neurophysiol. 83, 1019–1030. Fiser, J., Berkes, P., Orban, G., Lengyel, M., 2010. Statistically optimal perception and learning: from behavior to neural representations. Trends Cogn. Sci. 14, 119–130. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711. Freeman, W.J., 1975. Mass Action in the Nervous System: Examination of the Neurophysiological Basis of Adaptive Behavior Through the EEG. Academic Press, New York San Francisco London. Galarreta, M., Hestrin, S., 1998. Frequency-dependent synaptic depression and the balance of excitation and inhibition in the neocortex. Nat. Neurosci. 1, 587–594. Goedecke, I., Kim, D., Bonhoeffer, T., Singer, W., 1997. Development of orientation preference maps in area 18 of kitten visual cortex. Eur. J. Neurosci. 9, 1754–1762. Grinvald, A., Hildesheim, R., 2004. VSDI: a new area in functional imaging of cortical dynamics. Nat. Rev. Neurosci. 5, 874–885. Gros, C., 2009. Cognitive computation with autonomously active neural networks: an emerging field. Cogn. Comput. 1, 77–90. Hirsch, J., Gilbert, C.D., 1991. Synaptic physiology of horizontal connections in the cat's visual cortex. J. Neurosci. 11, 1800–1809. Honey, C.J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.P., Meuli, R., Hagmann, P., 2009. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. U. S. A. 106, 2035–2040. Hubel, D.H.,Wiesel, T.N., 1974. Uniformity of monkey striate cortex—parallel relationship between field size, scatter, and magnification factor. J. Comp. Neurol. 158, 295–306. Hubel, D.H., Wiesel, T.N., 1977. Functional architecture of macaque monkey visual cortex: Proceedings of the Royal Society of London Series B-Biological Sciences, 198 (1-&). Huberman, A.D., Feller, M.B., Chapman, B., 2008. Mechanisms underlying development of visual maps and receptive fields. Annu. Rev. Neurosci. 31, 479–509. Jansen, B.H., Rit, V.G., 1995. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled columns. Biol. Cybern. 73, 357–366. Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A., Arieli, A., 2003. Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956. Kiebel, S.J., Daunizeau, J., Friston, K.J., 2009a. Perception and hierarchical dynamics. Front. Neuroinform. 3, 20. Kiebel, S.J., von Kriegstein, K., Daunizeau, J., Friston, K.J., 2009b. Recognizing sequences of sequences. PLoS Comput. Biol. 5, e1000464. Kisvaday, Z., Martin, K., Freund, T., Magloczky, Z., Whitterige, D., Somogyi, P., 1986. Synaptic target of HRP-filled layer III pyramidal cells in the cat striate cortex. Exp. Brain Res. 64, 541–552. Koch, C., Poggio, T., 1985. The synaptic veto mechanism: does it underline direction and orientation selectivity in the visual cortex? In: Rose, D., Dobson, V. (Eds.), Models of the Visual Cortex. Wiley, New York, pp. 408–419. Koever, H., Bao, S., 2010. Cortical plasticity as a mechanism for storing Bayesian priors in sensory perception. PLoS One 5 (Article No.: e10497). Kohonen, T., 2001. Self-Organizing Maps. Springer . (Third, extended ed.). Komatsu, Y., Nakajima, S., Toyama, K., Fetz, E.E., 1988. Intracortical connectivity revealed by spike-triggered averaging in slice preparations of cat visual cortex. Brain Res. 442, 359–362. Loewel, S., Schmidt, K., Kim, D., Wolf, F., Hoffsuemmer, F., Singer, W., Bonhoeffer, T., 1998. The layout of orientation and ocular dominance domains in area 17 of strabismic cats. Eur. J. Neurosci. 10, 2629–2643. McCormick, D.A., 1999. Developmental neuroscience — spontaneous activity: signal or noise? Science 285, 541–543. McGuire, B., Gilbert, C., Rivlin, P., Wiesel, T., 1991. Targets of horizontal connections in macaque primary visual cortex. J. Comp. Neurol. 305, 370–392. Oja, E., 1982. Simplified neuron model as a principal component analyzer. J. Math. Biol. 15, 267–273. Rabinovich, M.I., Varona, P., Selverston, A.I., Abarbanel, H.D.I., 2006. Dynamical principles in neuroscience. Rev. Mod. Phys. 78, 1213–1265. Rabinovich, M.I., Huerta, R., Varona, P., Afraimovich, V.S., 2008. Transient cognitive dynamics, metastability, and decision making. PLoS Comput. Biol. 4, e1000072. Roerig, B., Katz, L., 1998. Relationships of synaptic input patterns to orientation preference maps in ferret visual cortex. Soc. Neurosci. 24. Sengpiel, F., Kind, P.C., 2002. The role of activity in development of the visual system. Curr. Biol. 12, R818–R826. Sernagor, E., Grzywacz, N.M., 1996. Influence of spontaneous activity and visual experience on developing retinal receptive fields. Curr. Biol. 6, 1503–1508. Spiegler, A., Kiebel, S.J., Atay, F.M., Knösche, T.R., 2010. Bifurcation analysis of neural mass models: impact of extrinsic inputs and dendritic time constants. NeuroImage 52, 1041–1058. Sur, M., Angelucci, A., Sharma, J., 1999. Rewiring cortex: the role of patterned activity in development and plasticity of neocortical circuits. J. Neurobiol. 41, 33–43. Thomson, A., Deuchars, J., 1994. Temporal and spatial properties of local circuits in neocortex. Trends Neurosci. 17, 119–126. Thomson, A., West, D., 1993. Fluctuations in pyramid–pyramid excitatory postsynaptic potentials modified by presynaptic firing pattern and postsynaptic membrane potential using paired intracellular recordings in rat neocortex. Neuroscience 54, 329–346. Toyama, K., Kimura, M., Tanaka, K., 1981. Organization of cat visual cortex as investigated by cross-correlation technique. J. Neurophysiol. 46, 202–214. Tucker, T., Katz, L., 1998. Organization of excitatory and inhibitory connections in layer 4 of ferret visual cortex. Soc. Neurosci. 24, 1756. Weliky, M., Kandler, K., Fitzpatrick, D., Katz, L., 1995. Pattern of excitation and inhibition evoked by horizontal connections in visual cortex share a common relationship to orientation columns. Neuron 15, 541–555. Wendling, F., Bellanger, J.J., Bartolomei, F., Chauvel, P., 2000. Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol. Cybern. 83, 367–378. Wilson, H.R., Cowan, J.D., 1973. Mathematical theory of functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13, 55–80. Zetterberg, L.H., Kristiansson, L., Mossberg, K., 1978. Performance of a model for a local neuron population. Biol. Cybern. 31, 15–26. Zhang, D., Raichle, M.E., 2010. Disease and the brain's dark energy. Nat. Rev. Neurol. 6, 15–28.

University Staff: Request a correction | Centaur Editors: Update this record

University of Reading

CentAUR: Central Archive at the University of Reading

Accessibility navigation

Associating spontaneous with evoked activity in a neural mass model of cat visual cortex

Abstract/Summary

Page navigation

See also

Footer navigation