Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-23T07:15:40.901Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamics and sources of response variability and its coordination in visual cortex

Published online by Cambridge University Press:  16 December 2019

Mahmood S. Hoseini Open the ORCID record for Mahmood S. Hoseini [Opens in a new window] ,
Nathaniel C. Wright ,
Ji Xia ,
Wesley Clawson ,
Woodrow Shew  and
Ralf Wessel
Mahmood S. Hoseini*
Affiliation:
Department of Physics, Washington University, St. Louis, Missouri
Nathaniel C. Wright
Affiliation:
Department of Physics, Washington University, St. Louis, Missouri
Ji Xia
Affiliation:
Department of Physics, Washington University, St. Louis, Missouri
Wesley Clawson
Affiliation:
Department of Electrical Engineering, University of Arkansas, Fayetteville, Arkansas
Woodrow Shew
Affiliation:
Department of Physics, University of Arkansas, Fayetteville, Arkansas
Ralf Wessel
Affiliation:
Department of Physics, Washington University, St. Louis, Missouri
*
*Address correspondence to: Mahmood S. Hoseini, Email: mhoseini@phy.ucsf.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

The trial-to-trial response variability in sensory cortices and the extent to which this variability can be coordinated among cortical units have strong implications for cortical signal processing. Yet, little is known about the relative contributions and dynamics of defined sources to the cortical response variability and their correlations across cortical units. To fill this knowledge gap, here we obtained and analyzed multisite local field potential (LFP) recordings from visual cortex of turtles in response to repeated naturalistic movie clips and decomposed cortical across-trial LFP response variability into three defined sources, namely, input, network, and local fluctuations. We found that input fluctuations dominate cortical response variability immediately following stimulus onset, whereas network fluctuations dominate the response variability in the steady state during continued visual stimulation. Concurrently, we found that the network fluctuations dominate the correlations of the variability during the ongoing and steady-state epochs, but not immediately following stimulus onset. Furthermore, simulations of various model networks indicated that (i) synaptic time constants, leading to oscillatory activity, and (ii) synaptic clustering and synaptic depression, leading to spatially constrained pockets of coherent activity, are both essential features of cortical circuits to mediate the observed relative contributions and dynamics of input, network, and local fluctuations to the cortical LFP response variability and their correlations across recording sites. In conclusion, these results show how a mélange of multiscale thalamocortical circuit features mediate a complex stimulus-modulated cortical activity that, when naively related to the visual stimulus alone, appears disguised as high and coordinated across-trial response variability.

Keywords

Response variabilityVisual cortexTurtleCorrelationLocal field potential
Type
Research Article
Information
Visual Neuroscience , Volume 36 , 2019 , E013
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

Current address: Center for Integrative Neuroscience, Department of Physiology, 675 Nelson Rising Lane, University of California, San Francisco, California.

References

Achuthan, S. & Canavier, C.C. (2009). Phase-resetting curves determine synchronization, phase locking, and clustering in networks of neural oscillators. Journal of Neuroscience 29, 5218–5233.CrossRefGoogle ScholarPubMed
Agarwal, G., Stevenson, I.H., Berényi, A., Mizuseki, K., Buzsáki, G. & Sommer, F.T. (2014). Spatially distributed local fields in the hippocampus encode rat position. Science 344, 626–630.CrossRefGoogle ScholarPubMed
Aguirre, C., Huerta, R., Corbacho, F. & Pascual, P. (2002). Analysis of biologically inspired small-world networks. In International Conference on Artificial Neural Networks, pp. 27–32. Springer: Berlin, Heidelberg.Google Scholar
Arandia-Romero, I., Tanabe, S., Drugowitsch, J., Kohn, A. & Moreno-Bote, R. (2016). Multiplicative and additive modulation of neuronal tuning with population activity affects encoded information. Neuron 89, 1305–1316.CrossRefGoogle ScholarPubMed
Averbeck, B.B., Latham, P.E. & Pouget, A. (2006). Neural correlations, population coding and computation. Nature Reviews Neuroscience 7, 358–366.CrossRefGoogle ScholarPubMed
Averbeck, B.B. & Lee, D. (2004). Coding and transmission of information by neural ensembles. Trends in Neurosciences 27, 225–230.CrossRefGoogle ScholarPubMed
Balaban, C.D. (1978). Structure of anterior dorsal ventricular ridge in a turtle (Pseudemys scripta elegans). Journal of Morphology 158, 291–322.CrossRefGoogle Scholar
Balaguer-Ballester, E. (2017) Cortical variability and challenges for modeling approaches. Frontiers in Systems Neuroscience 11. doi: 10.3389/fnsys.2017.00015.CrossRefGoogle ScholarPubMed
Brunel, N. & Wang, X.-J. (2003). What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. Journal of Neurophysiology 90, 415–430.CrossRefGoogle ScholarPubMed
Bujan, A.F., Aertsen, A. & Kumar, A. (2015). Role of input correlations in shaping the variability and noise correlations of evoked activity in the neocortex. Journal of Neuroscience 35, 8611–8625.CrossRefGoogle ScholarPubMed
Buzsáki, G., Anastassiou, C.A. & Koch, C. (2012). The origin of extracellular fields and currents—EEG, ECoG, LFP, and spikes. Nature Reviews Neuroscience 13, 407–420.CrossRefGoogle ScholarPubMed
Carandini, M. (2004). Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biology 2, E264.CrossRefGoogle ScholarPubMed
Chiappe, M.E., Seelig, J.D., Reiser, M.B. & Jayaraman, V. (2010). Walking modulates speed sensitivity in Drosophila motion vision. Current Biology 20, 1470–1475.CrossRefGoogle ScholarPubMed
Churchland, M.M., Yu, B.M., Cunningham, J.P., Sugrue, L.P., Cohen, M.R., Corrado, G.S., Newsome, W.T., Clark, A.M., Hosseini, P., Scott, B.B., Bradley, D.C., Smith, M.A., Kohn, A., Movshon, J.A., Armstrong, K.M., Moore, T., Chang, S.W., Snyder, L.H., Lisberger, S.G., Priebe, N.J., Finn, I.M., Ferster, D., Ryu, S.I, Santhanam, G., Sahani, M. & Shenoy, K.V. (2010). Stimulus onset quenches neural variability: A widespread cortical phenomenon. Nature Neuroscience 13, 369–378.CrossRefGoogle ScholarPubMed
Clawson, W.P., Wright, N.C., Wessel, R. & Shew, W.L. (2017). Adaptation towards scale-free dynamics improves cortical stimulus discrimination at the cost of reduced detection. PLoS Computational Biology 13, e1005574.CrossRefGoogle ScholarPubMed
Cohen-Kashi Malina, K., Malina, K.C.-K., Mohar, B., Rappaport, A.N. & Lampl, I. (2016). Local and thalamic origins of correlated ongoing and sensory-evoked cortical activities. Nature Communications 7, 12740.CrossRefGoogle ScholarPubMed
Cohen, M.R. & Kohn, A. (2011). Measuring and interpreting neuronal correlations. Nature Neuroscience 14, 811–819.CrossRefGoogle ScholarPubMed
Cossell, L., Iacaruso, M.F., Muir, D.R., Houlton, R., Sader, E.N., Ko, H., Hofer, S.B. & Mrsic-Flogel, T.D. (2015). Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399–403.CrossRefGoogle ScholarPubMed
Cunningham, J.P. & Yu, B.M. (2014). Dimensionality reduction for large-scale neural recordings. Nature Neuroscience 17, 1500.CrossRefGoogle ScholarPubMed
Curto, C., Sakata, S., Marguet, S., Itskov, V. & Harris, K.D. (2009). A simple model of cortical dynamics explains variability and state dependence of sensory responses in urethane-anesthetized auditory cortex. Journal of Neuroscience 29, 10600–10612.CrossRefGoogle ScholarPubMed
Dakin, S. & Frith, U. (2005). Vagaries of visual perception in autism. Neuron 48, 497–507.CrossRefGoogle ScholarPubMed
David, S.V. & Gallant, J.L. (2005). Predicting neuronal responses during natural vision. Network: Computation in Neural Systems 16, 239–260.CrossRefGoogle ScholarPubMed
Dayan, P. & Abbott, L. (2003). Theoretical neuroscience: Computational and mathematical modeling of neural systems Journal of Cognitive Neuroscience 15, 154–155.CrossRefGoogle Scholar
Deco, G. & Hugues, E. (2012). Neural network mechanisms underlying stimulus driven variability reduction. PLoS Computational Biology 8, e1002395.CrossRefGoogle ScholarPubMed
de la Rocha, J., Doiron, B., Shea-Brown, E., Josić, K. & Reyes, A. (2007). Correlation between neural spike trains increases with firing rate. Nature 448, 802–806.CrossRefGoogle ScholarPubMed
Desan, P.H. (1984). The organization of the cerebral cortex of the pond turtle, Pseudemys scripta elegans (Doctoral dissertation). Harvard University, Cambridge, MA.Google Scholar
Doiron, B., Litwin-Kumar, A., Rosenbaum, R., Ocker, G.K. & Josić, K. (2016). The mechanics of state-dependent neural correlations. Nature Neuroscience 19, 383–393.CrossRefGoogle ScholarPubMed
Ecker, A.S., Berens, P., James Cotton, R., Subramaniyan, M., Denfield, G.H., Cadwell, C.R., Smirnakis, S.M., Bethge, M. & Tolias, A.S. (2014). State dependence of noise correlations in macaque primary visual cortex. Neuron 82, 235–248.CrossRefGoogle ScholarPubMed
Einevoll, G.T., Kayser, C., Logothetis, N.K. & Panzeri, S. (2013). Modelling and analysis of local field potentials for studying the function of cortical circuits. Nature Reviews Neuroscience 14, 770–785.CrossRefGoogle ScholarPubMed
Ermentrout, B. (1996). Type I membranes, phase resetting curves, and synchrony. Neural Computation 8, 979–1001.CrossRefGoogle ScholarPubMed
Faisal, A.A., Aldo Faisal, A., Selen, L.P.J. & Wolpert, D.M. (2008). Noise in the nervous system. Nature Reviews Neuroscience 9, 292–303.CrossRefGoogle Scholar
Findlay, J.M. & Gilchrist, I.D. (2003). Active Vision: The Psychology of Looking and Seeing (Oxford University Press).CrossRefGoogle Scholar
Findlay, J.M., Findlay, J.M. & Gilchrist, I.D.(2003). Active Vision: The Psychology of Looking and Seeing (No. 37). Oxford University Press, Oxford.CrossRefGoogle Scholar
Foffani, G. & Moxon, K.A. (2004). PSTH-based classification of sensory stimuli using ensembles of single neurons. Journal of Neuroscience Methods 135, 107–120.CrossRefGoogle ScholarPubMed
Goris, R.L.T., Movshon, J.A. & Simoncelli, E.P. (2014). Partitioning neuronal variability. Nature Neuroscience 17, 858–865.CrossRefGoogle ScholarPubMed
Gutnisky, D.A. & Dragoi, V. (2008). Adaptive coding of visual information in neural populations. Nature 452, 220–224.CrossRefGoogle ScholarPubMed
Haefner, R.M. & Cumming, B.G. (2008). An improved estimator of Variance Explained in the presence of noise. Advances in Neural Information Processing Systems 2008, 585–592.Google ScholarPubMed
Haider, B., Schulz, D.P.A., Häusser, M. & Carandini, M. (2016). Millisecond coupling of local field potentials to synaptic currents in the awake visual cortex. Neuron 90, 35–42.CrossRefGoogle ScholarPubMed
Harris, K.D. & Thiele, A. (2011). Cortical state and attention. Nature Reviews Neuroscience 12, 509–523.CrossRefGoogle ScholarPubMed
Hoseini, M.S., Pobst, J., Wright, N.C., Clawson, W., Shew, W. & Wessel, R. (2017a). The turtle visual system mediates a complex spatiotemporal transformation of visual stimuli into cortical activity. Journal of Comparative Physiology 204, 167–181.CrossRefGoogle Scholar
Hoseini, M.S., Pobst, J., Wright, N., Clawson, W., Shew, W. & Wessel, R. (2017b). Induced cortical oscillations in turtle cortex are coherent at the mesoscale of population activity, but not at the microscale of the membrane potential of neurons. Journal of Neurophysiology 118, 2579–2591.CrossRefGoogle Scholar
Hoseini, M.S. & Wessel, R. (2016). Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons. Journal of Neurophysiology 115, 457–469.CrossRefGoogle ScholarPubMed
Hounsgaard, J. & Nicholson, C. (1990). The isolated turtle brain and the physiology of neuronal circuits. In Preparations of Vertebrate Central Nervous System in Vitro, pp. 155–181. John Wiley & Sons, Chichester.Google Scholar
Huang, C., Ruff, D.A., Pyle, R., Rosenbaum, R., Cohen, M.R. & Doiron, B. (2019). Circuit models of low-dimensional shared variability in cortical networks. Neuron 101, 337–348.CrossRefGoogle ScholarPubMed
Ji, N., Freeman, J. & Smith, S.L. (2016). Technologies for imaging neural activity in large volumes. Nature Neuroscience 19, 1154–1164.CrossRefGoogle ScholarPubMed
Kappenman, E.S. & Luck, S.J. (2010). The effects of electrode impedance on data quality and statistical significance in ERP recordings. Psychophysiology 47, 888–904.Google ScholarPubMed
Karimipanah, Y., Ma, Z., Miller, J.-E.K., Yuste, R. & Wessel, R. (2017). Neocortical activity is stimulus- and scale-invariant. PLoS One 12, e0177396.CrossRefGoogle Scholar
Kelly, R.C., Smith, M.A., Kass, R.E. & Lee, T.S. (2010). Local field potentials indicate network state and account for neuronal response variability. Journal of Computational Neuroscience 29, 567–579.CrossRefGoogle ScholarPubMed
Kim, T.H., Zhang, Y., Lecoq, J., Jung, J.C., Li, J., Zeng, H., Niell, C.M. & Schnitzer, M.J. (2016). Long-term optical access to an estimated one million neurons in the live mouse cortex. Cell Reports 17, 3385–3394.CrossRefGoogle Scholar
Land, M.F. & Tatler, B.W. (2009). Looking and Acting: Vision and Eye Movements in Natural Behaviour. Oxford University Press, Oxford.CrossRefGoogle Scholar
Lin, I.-C., Okun, M., Carandini, M. & Harris, K.D. (2015). The nature of shared cortical variability. Neuron 87, 644–656.CrossRefGoogle ScholarPubMed
Lindén, H., Tetzlaff, T., Potjans, T.C., Pettersen, K.H., Grün, S., Diesmann, M. & Einevoll, G.T. (2011). Modeling the spatial reach of the LFP. Neuron 72, 859–872.CrossRefGoogle ScholarPubMed
Litwin-Kumar, A. & Doiron, B. (2012). Slow dynamics and high variability in balanced cortical networks with clustered connections. Nature Neuroscience 15, 1498–1505.CrossRefGoogle ScholarPubMed
Llinás, R., Yarom, Y. & Sugimori, M. (1981). Isolated mammalian brain in vitro: New technique for analysis of electrical activity of neuronal circuit function. Federation Proceedings 40, 2240–2245.Google ScholarPubMed
Maass, W. & Orponen, P. (1998). On the effect of analog noise in discrete-time analog computations. Neural Computation 10, 1071–1095.CrossRefGoogle Scholar
Macrides, F. & Chorover, S.L. (1972). Olfactory bulb units: Activity correlated with inhalation cycles and odor quality. Science 175, 84–87.CrossRefGoogle ScholarPubMed
Moreno-Bote, R., Beck, J., Kanitscheider, I., Pitkow, X., Latham, P. & Pouget, A. (2014). Information-limiting correlations. Nature Neuroscience 17, 1410–1417.CrossRefGoogle ScholarPubMed
Niell, C.M. & Stryker, M.P. (2010). Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472–479.CrossRefGoogle Scholar
Ohiorhenuan, I.E., Mechler, F., Purpura, K.P., Schmid, A.M., Hu, Q. & Victor, J.D. (2010). Sparse coding and high-order correlations in fine-scale cortical networks. Nature 466, 617–621.CrossRefGoogle ScholarPubMed
Okun, M., Steinmetz, N., Cossell, L., Iacaruso, M.F., Ko, H., Barthó, P., Moore, T., Hofer, S.B., Mrsic-Flogel, T.D., Carandini, M. & Harris, K.D. (2015). Diverse coupling of neurons to populations in sensory cortex. Nature 521, 511–515.CrossRefGoogle ScholarPubMed
Ollerenshaw, D.R., Zheng, H.J., Millard, D.C., Wang, Q. & Stanley, G.B. (2014). The adaptive trade-off between detection and discrimination in cortical representations and behavior. Neuron 81, 1152–1164.CrossRefGoogle ScholarPubMed
Panzeri, S., Brunel, N., Logothetis, N.K. & Kayser, C. (2010). Sensory neural codes using multiplexed temporal scales. Trends in Neurosciences 33, 111–120.CrossRefGoogle ScholarPubMed
Poulet, J.F.A. & Petersen, C.C.H. (2008). Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature 454, 881–885.CrossRefGoogle ScholarPubMed
Priebe, N.J. & Ferster, D. (2012). Mechanisms of neuronal computation in mammalian visual cortex. Neuron 75, 194–208.CrossRefGoogle ScholarPubMed
Quiroga, R.Q., Kraskov, A., Kreuz, T. & Grassberger, P. (2002). Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Physical Review E - Statistical, Nonlinear and Soft Matter Physics 65, 041903.CrossRefGoogle Scholar
Quiroga, R.Q. & Panzeri, S. (2009). Extracting information from neuronal populations: Information theory and decoding approaches. Nature Reviews Neuroscience 10, 173.CrossRefGoogle Scholar
Reimann, M.W., Anastassiou, C.A., Perin, R., Hill, S.L., Markram, H. & Koch, C. (2013). A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron 79, 375–390.CrossRefGoogle ScholarPubMed
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A. & Harris, K.D. (2010). The asynchronous state in cortical circuits. Science 327, 587–590.CrossRefGoogle Scholar
Renart, A. & Machens, C.K. (2014). Variability in neural activity and behavior. Current Opinion in Neurobiology 25, 211–220.CrossRefGoogle ScholarPubMed
Rikhye, R.V. & Sur, M. (2015). Spatial correlations in natural scenes modulate response reliability in mouse visual cortex. Journal of Neuroscience 35, 14661–14680.CrossRefGoogle ScholarPubMed
Rosenbaum, R.J. (2010). Pooling and correlated neural activity. Frontiers in Computational Neuroscience 4, 9.Google ScholarPubMed
Rosenbaum, R.J., Smith, M.A., Kohn, A., Rubin, J.E. & Doiron, B. (2017). The spatial structure of correlated neuronal variability. Nature Neuroscience 20, 107.CrossRefGoogle ScholarPubMed
Sadagopan, S. & Ferster, D. (2012). Feedforward origins of response variability underlying contrast invariant orientation tuning in cat visual cortex. Neuron 74, 911–923.CrossRefGoogle ScholarPubMed
Salinas, E. & Sejnowski, T.J. (2000). Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. Journal of Neuroscience 20, 6193–6209.CrossRefGoogle ScholarPubMed
Scholvinck, M.L., Saleem, A.B., Benucci, A., Harris, K.D. & Carandini, M. (2015). Cortical state determines global variability and correlations in visual cortex. Journal of Neuroscience 35, 170–178.CrossRefGoogle ScholarPubMed
Senzai, Y., Fernandez-Ruiz, A. & Buzsáki, G. (2019). Layer-specific physiological features and interlaminar interactions in the primary visual cortex of the mouse. Neuron 101, 500–513.e5.CrossRefGoogle ScholarPubMed
Shadlen, M.N. & Newsome, W.T. (1998). The variable discharge of cortical neurons: Implications for connectivity, computation, and information coding. Journal of Neuroscience 18, 3870–3896.CrossRefGoogle ScholarPubMed
Shew, W.L., Clawson, W.P., Pobst, J., Karimipanah, Y., Wright, N.C. & Wessel, R. (2015). Adaptation to sensory input tunes visual cortex to criticality. Nature Physics 11, 659–663.CrossRefGoogle Scholar
Shusterman, R., Smear, M.C., Koulakov, A.A. & Rinberg, D. (2011). Precise olfactory responses tile the sniff cycle. Nature Neuroscience 14, 1039–1044.CrossRefGoogle ScholarPubMed
Swanson, L.W. (2003). Brain Architecture: Understanding the Basic Plan. New York: Oxford University Press.Google Scholar
Tan, A.Y.Y., Chen, Y., Scholl, B., Seidemann, E. & Priebe, N.J. (2014). Sensory stimulation shifts visual cortex from synchronous to asynchronous states. Nature 509, 226–229.CrossRefGoogle ScholarPubMed
Torrence, C. & Compo, G.P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79, 61–78.2.0.CO;2>CrossRefGoogle Scholar
Ulinski, P.S. (1990). The cerebral cortex of reptiles. In Comparative Structure and Evolution of Cerebral Cortex Part I, pp. 139–215. Springer, Boston, MA.Google Scholar
Van Vreeswijk, C. & Sompolinsky, H. (1996). Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724–1726.CrossRefGoogle ScholarPubMed
Vogels, R., Spileers, W. & Orban, G.A. (1989). The response variability of striate cortical neurons in the behaving monkey. Experimental Brain Research 77, 432–436.CrossRefGoogle ScholarPubMed
Wang, X.-J. (1999). Synaptic basis of cortical persistent activity: The importance of NMDA receptors to working memory. Journal of Neuroscience 19, 9587–9603.CrossRefGoogle ScholarPubMed
Wang, X.-J. (2010). Neurophysiological and computational principles of cortical rhythms in cognition. Physiological Reviews 90, 1195–1268.CrossRefGoogle ScholarPubMed
Watts, D.J. & Strogatz, S.H. (1998). Collective dynamics of ‘small-world’ networks. Nature 393, 440–442.CrossRefGoogle ScholarPubMed
Wright, N.C., Hoseini, M.S. & Wessel, R. (2017a). Adaptation modulates correlated subthreshold response variability in visual cortex. Journal of Neurophysiology 118, 1257–1269.CrossRefGoogle Scholar
Wright, N.C., Hoseini, M.S., Yasar, T.B. & Wessel, R. (2017b). Coupling of synaptic inputs to local cortical activity differs among neurons and adapts after stimulus onset. Journal of Neurophysiology 118, 3345–3359.CrossRefGoogle Scholar
Wright, N.C. & Wessel, R. (2017). Network activity influences the subthreshold and spiking visual responses of pyramidal neurons in the three-layer turtle cortex. Journal of Neurophysiology 118, 2142–2155.CrossRefGoogle ScholarPubMed
Yang, H., Shew, W.L., Roy, R. & Plenz, D. (2012). Maximal variability of phase synchrony in cortical networks with neuronal avalanches. Journal of Neuroscience 32, 1061–1072.CrossRefGoogle ScholarPubMed
Yu, B.M., Cunningham, J.P., Santhanam, G., Ryu, S.I., Shenoy, K.V. & Sahani, M. (2009). Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Journal of Neurophysiology 102, 614–635.CrossRefGoogle ScholarPubMed
Zohary, E., Shadlen, M.N. & Newsome, W.T. (1994). Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140–143.CrossRefGoogle ScholarPubMed