UvA-DARE is a service provided by the library of the University of Amsterdam (http://dare.uva.nl)

UvA-DARE (Digital Academic Repository)

More than meets the eyeProcessing of visual and auditory information in the sensory cortexMeijer, G.T.

Link to publication

Creative Commons License (see https://creativecommons.org/use-remix/cc-licenses):Other

Citation for published version (APA):Meijer, G. T. (2019). More than meets the eye: Processing of visual and auditory information in the sensorycortex.

General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s),other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons).

Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, statingyour reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Askthe Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam,The Netherlands. You will be contacted as soon as possible.

Download date: 28 Jan 2020

AppendicesSummary

Nederlandstalige samenvatting

Bibliography

List of publications and posters

Contributions of co-authors

About the author

Acknowledgements

191

Appendices

SUMMARYIn this thesis, titled ‘More than meets the eye: processing of visual and auditory information in the sensory cortex’, I investigated the behavioral and neuronal underpinnings of the integration of vision and audition by addressing these specific research questions: (i) how does sound influence the pro-cessing of visual information in the primary visual cortex? (ii) Do mice integrate visual and auditory information during stimulus detection to increase their behavioral performance? (iii) What are the neural correlates of audiovisual detection behavior and which visual areas are involved? And lastly, (iv) how do neural circuits in the visual cortex cope with the substantial variability of single neurons while processing such sensory information? I set out to answer these questions using a combination of behavioral assessments and the probing of neural activity in sensory areas of the mouse.

The first research question we addressed (Chapter 2) was driven by two key observations in the liter-ature: Iurilli, et al. (2012) showed a suppression of V1 activity by sound whereas Ibrahim, et al. (2016) showed an enhancement of V1 responses when sound was presented. Therefore, in Chapter 2 we hypothesized that the influence of sound on visual processing does not always follow the same rules but is depended upon a number of factors such as the intensity and the configuration of the stimulus. To investigate this hypothesis, we performed two-photon calcium imaging in superficial layers of V1 of the awake mouse while presenting visual paired with auditory stimuli. We found two factors which contributed to coding of audiovisual stimuli in V1: the intensity of the stimulus and whether the audiovisual components of the stimulus were temporally congruent with one another. When the audiovisual stimulus was congruent (same visual and auditory temporal frequency) response enhance-ment and suppression were always balanced, regardless of the stimulus intensity. Conversely, when the auditory and visual components of the stimulus were incongruent, V1 neurons were suppressed when the stimulus intensity was high but enhanced when it was low. This showed that, indeed, wheth-er V1 is activated or suppressed by sound depends upon the specific stimulus that is being presented.

Whether these effects, which occur on a neuronal level, actually pertain to a behavioral outcome was as of yet unknown. To shed light on this issue, we first developed a behavioral task in which mice detected visual, auditory and audiovisual stimuli (Chapter 3). When cross-modal stimuli are clearly perceivable there is arguably no need for the animal to integrate them because the cross-modal stim-ulus can be easily detected by just processing one of the stimulus constituents. When the stimulus is very faint and hard to detect, however, we hypothesized that integrating the visual and auditory component of the cross-modal stimulus would result in increased perceptual performance. Evidently, we found that mice showed elevated detection performance when audiovisual stimuli were presented at the intensity level of the detection threshold of the animal compared to modality-specific stimuli. This increase in behavioral performance, however, can also be explained by stimulus redundancy: in the cross-modal condition two stimuli are presented whereas in the modality-specific condition only a single stimulus appears. The chances of detecting a compound stimulus are therefore higher than detecting a single stimulus because the two components of the audiovisual stimulus can be detected

192

A

independently. To uncover which strategy the mice used, we fitted signal detection theory models to the behavioral data which showed that the most mice did not perform independent detection of either the visual or auditory component of the stimulus, but integrated these constituents into a single combined stimulus.

With this behavioral paradigm in our hands we could revisit the question posed earlier: what are the neural mechanisms underlaying audiovisual integration? In Chapter 4, we trained mice to perform the audiovisual detection task outlined in Chapter 3, while mice were performing this task we performed calcium imaging in superficial layers of V1 and a putative site of audiovisual integration: area AL, situated in between V1 and A1 (Wang and Burkhalter, 2007). We found the balance between response enhancement and suppression, discovered in Chapter 2, in both V1 and AL. However, the neurons which showed a multisensory modulation of their response when the stimulus was easily detectable were not the same ones that were modulated when the stimulus was presented around the perceptual threshold of the mouse. This indicates that multisensory integration does not happen automatically in neural circuits, but depends on the sensory context. Furthermore, we found neurons which special-ized in the coding of the behavioral choice of the animal solely in the multisensory condition. These ‘multisensory choice neurons’ were found more in AL compared to V1 and neural populations in area AL showed a stronger a stronger neural correlate of behavioral detection of multisensory stimuli compared to V1. This suggests that area AL is part of the multisensory pathway of sensori-motor transformation.

In Chapter 5 we addressed a long-standing question in system neuroscience: how does correlated trial-to-trial variability, so-called noise correlations, impact the efficacy of population coding? We performed chronic two-photon imaging of neuronal populations in the primary visual cortex of the awake mouse while presenting drifting grating and natural movie stimuli. We found that trial-to-trial variability was mostly confined parallel to decision boundaries, especially in high-dimensional neural space. This means that noise correlations aid the readout of population level information. Moreover, we show that these higher-order correlations are stable over time and that a substantial fraction of trial-to-trial variability of single neurons is not noise but can be predicted from the rest of the pop-ulation.

193

Appendices

NEDERLANDSTALIGE SAMENVATTINGDe wereld om ons heen ervaren we via al onze zintuigen. Als we een koffie kopje op de grond kapot zien vallen, wordt het beeld van het brekende kopje vanzelf verbonden met het geluid van brekend aardewerk. Deze integratie verloopt in onze ervaring volledig automatisch en zonder enige moeite. Echter, de integratie van informatie uit verschillende zintuigen is een complex proces en het is nog grotendeels onbekend hoe dit werkt op een neuronaal niveau. Sensorische informatie wordt ver-zameld door onze zintuigen en vervolgens naar gespecializeerde hersengebieden gestuurd. Visuele informatie komt als eerste de neocortex binnen in de primaire visuele cortex (V1). Er werd lang ge-dacht dat dit gebied enkel visuele informatie verwerkt, en dat de integratie van verschillende zintuigen optrad in gespecializeerde integratie gebieden. Onze hypothese was echter dat al in V1 informatie van andere zintuigen geintegreerd wordt.

Dit onderzochten we door een activiteits-afhankelijk eiwit tot expressie te brengen in neuronen in V1 van een muis. Door de veranderingen in fluorescentie op te nemen kon de activiteit van een grote groep neuronen gemeten worden. Vervolgens toonden we visuele, auditieve en gecombineerde audio-visuele stimuli aan de muis en keken we naar de activiteit van een populatie V1 neuronen. We vonden dat, in lijn met onze hypothese, V1 niet alleen visuele maar ook auditieve informatie verwerkt (Chapter 2). Neuronen in V1 moduleerden hun activiteit als een visuele stimulus gepaard ging met een auditieve stimulus, en we vonden neuronen in V1 die reageerden op alleen geluid, zonder enige visuele input. Het was ook van belang of de visuele en auditieve stimuli bij elkaar pasten: als dit niet het geval was, en de visuele en auditieve stimulus incongruent met elkaar waren, werden neuronen in V1 onderdrukt in hun activiteit.

Het tonen van visuele en auditieve stimuli aan een muis betekent echter niet dat deze stimuli ook daadwerkelijk geintegreerd worden door het dier. Om zeker te weten dat de muizen de stimuli niet alleen waarnamen, maar ook integreerden, hebben we ze geleerd om aan te geven wanneer ze een visuele, auditieve of audio-visuele stimulus waarnamen. Vervolgens maakten we deze taak moeilijker door de stimuli steeds zwakker te maken totdat deze bijna niet meer waarneembaar waren. In deze situatie werd het voor het dier gunstig om visuele en auditieve informatie te integreren omdat ze op zichzelf te zwak waren om waar te nemen (Chapter 3). Om de neurale processen te onderzoeken die aan dit proces ten grondslag liggen, maten we de activiteit van neuronen in V1 en in AL (een associ-atie gebied tussen de visuele en auditieve cortex in) terwijl de muis deze taak uitvoerde. We vonden dat, in vergelijking met V1, AL sterker reageerde op hele zwakke visuele stimuli. De neurale activiteit in AL matchte precies met het gedrag van het dier. Verder vonden we een neuraal correlaat in V1 met het detecteren van visuele stimuli en in AL met het detecteren van multisensorische stimuli. Dat gaf aan dat dit gebied hoogstwaarschijnlijk betrokken is bij multisensorische integratie van audio-visuele stimuli tijdens het actief detecteren van deze stimuli (Chapter 4).

194

A

Als laatst gingen we terug naar V1 om een fundamentele vraag te stellen over de verwerking van sen-sorische informatie. Als meerdere keren precies dezelfde stimulus wordt getoond, reageren neuronen in V1 elke keer met een andere activiteit. Hoe kan het dat deze variabiliteit niet ertoe leidt dat het brein de hele tijd fouten maakt tijdens het verwerken van informatie? We maten opnieuw neuronen in V1 terwijl we een breed scala aan visuele stimuli lieten zien over een lange periode. Met behulp van populatie analyses toonden we aan dat neurale variabiliteit in hogere dimensies gelimiteerd blijft tot een ruimte waarin deze de codering van verschillende stimuli niet belemmert. In andere woorden, wat op het niveau van een enkel neuron op ongestructureerde variabiliteit lijkt, heeft op het niveau van een grote groep neuronen wel een structuur. Sterker nog, een structuur die de populatie code helpt bij het onderscheiden van verschillende stimuli (Chapter 5). Alles bij elkaar genomen heb ik in deze thesis een belangrijke stap gezet in ons begrip over hoe de sensorische cortex visuele en multisensorische informatie verwerkt.

195

Appendices

BIBLIOGRAPHYAarts, E., Verhage, M., Veenvliet, J.V., Dolan,

C.V., and Sluis, S. van der (2014). A solution to dependency: using multilevel analysis to accommodate nested data. Nature Neuroscience 17, 491–496.

Abbott, L.F., and Dayan, P. (1999). The Effect of Correlated Variability on the Accuracy of a Population Code. Neural Computation 11, 91–101.

Ahrens, S., Jaramillo, S., Yu, K., Ghosh, S., Hwang, G.-R., Paik, R., Lai, C., He, M., Huang, Z.J., and Li, B. (2015). ErbB4 regulation of a thalamic reticular nucleus circuit for sensory selection. Nat. Neurosci. 18, 104–111.

Allen, W.E., Kauvar, I.V., Chen, M.Z., Richman, E.B., Yang, S.J., Chan, K., Gradinaru, V., Deverman, B.E., Luo, L., and Deisseroth, K. (2017). Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex. Neuron 94, 891-907.e6.

Alvarado, J.C., Vaughan, J.W., Stanford, T.R., and Stein, B.E. (2007). Multisensory versus unisensory integration: contrasting modes in the superior colliculus. J. Neurophysiol. 97, 3193–3205.

Andermann, M.L., Kerlin, A.M., Roumis, D.K., Glickfeld, L.L., and Reid, R.C. (2011). Functional Specialization of Mouse Higher Visual Cortical Areas. Neuron 72, 1025–1039.

Andersen, R.A., Snyder, L.H., Bradley, D.C., and Xing, J. (1997). Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annu. Rev. Neurosci. 20, 303–330.

Aston-Jones, G., and Cohen, J.D. (2005). An Integrative Theory of Locus Coeruleus-Norepinephrine Function: Adaptive Gain and Optimal Performance. Annual Review of Neuroscience 28, 403–450.

van Atteveldt, N., Formisano, E., Goebel, R.,

and Blomert, L. (2004). Integration of Letters and Speech Sounds in the Human Brain. Neuron 43, 271–282.

van Atteveldt, N., Murray, M.M., Thut, G., and Schroeder, C.E. (2014). Multisensory integration: flexible use of general operations. Neuron 81, 1240–1253.

Averbeck, B.B., and Lee, D. (2006). Effects of Noise Correlations on Information Encoding and Decoding. Journal of Neurophysiology 95, 3633–3644.

Averbeck, B.B., Latham, P.E., and Pouget, A. (2006). Neural correlations, population coding and computation. Nature Reviews Neuroscience 7, 358–366.

Avillac, M., Denève, S., Olivier, E., Pouget, A., and Duhamel, J.-R. (2005). Reference frames for representing visual and tactile locations in parietal cortex. Nat Neurosci 8, 941–949.

Baddeley, R., Abbott, L.F., Booth, M.C.A., Sengpiel, F., Freeman, T., Wakeman, E.A., and Rolls, E.T. (1997). Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc. R. Soc. Lond. B 264, 1775–1783.

Banks, M.I., Uhlrich, D.J., Smith, P.H., Krause, B.M., and Manning, K.A. (2011). Descending Projections from Extrastriate Visual Cortex Modulate Responses of Cells in Primary Auditory Cortex. Cereb. Cortex 21, 2620–2638.

Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67.

Baum, S.H., Stevenson, R.A., and Wallace, M.T. (2015). Behavioral, perceptual, and neural alterations in sensory and multisensory function in autism spectrum disorder. Prog. Neurobiol. 134, 140–160.

Beauchamp, M.S. (2005). See me, hear me, touch me: multisensory integration in lateral occipital-temporal cortex. Curr.

196

A

Opin. Neurobiol. 15, 145–153.

Beck, J.M., Ma, W.J., Kiani, R., Hanks, T., Churchland, A.K., Roitman, J., Shadlen, M.N., Latham, P.E., and Pouget, A. (2008). Probabilistic Population Codes for Bayesian Decision Making. Neuron 60, 1142–1152.

Bell, A.H., Meredith, M.A., Van Opstal, A.J., and Munoz, D.P. (2005). Crossmodal integration in the primate superior colliculus underlying the preparation and initiation of saccadic eye movements. J. Neurophysiol. 93, 3659–3673.

Benevento, L.A., Fallon, J., Davis, B.J., and Rezak, M. (1977). Auditory-visual interaction in single cells in the cortex of the superior temporal sulcus and the orbital frontal cortex of the macaque monkey. Experimental Neurology 57, 849–872.

Berens, P. (2008). Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex. Frontiers in Systems Neuroscience 2.

Berens, P., Freeman, J., Deneux, T., Chenkov, N., McColgan, T., Speiser, A., Macke, J.H., Turaga, S.C., Mineault, P., Rupprecht, P., et al. (2018). Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLOS Computational Biology 14, e1006157.

Birch, L.M., Warfield, D., Ruben, R.J., and Mikaelian, D.O. (1968). Behavioral measurements of pure tone thresholds in normal CBA-J mice. Journal of Auditory Research 8, 459–468.

Birrell, J.M., and Brown, V.J. (2000). Medial frontal cortex mediates perceptual attentional set shifting in the rat. J. Neurosci. 20, 4320–4324.

Bizley, J.K., and King, A.J. (2008). Visual-auditory spatial processing in auditory cortical neurons. Brain Res. 1242, 24–36.

Bizley, J.K., Nodal, F.R., Bajo, V.M., Nelken, I., and King, A.J. (2007). Physiological and Anatomical Evidence for Multisensory Interactions in Auditory Cortex. Cereb. Cortex 17, 2172–2189.

Bizley, J.K., Shinn-Cunningham, B.G., and Lee, A.K.C. (2012). Nothing Is Irrelevant in a Noisy World: Sensory Illusions Reveal Obligatory within-and across-Modality Integration. Journal of Neuroscience 32, 13402–13410.

Bizley, J.K., Maddox, R.K., and Lee, A.K.C. (2016). Defining Auditory-Visual Objects: Behavioral Tests and Physiological Mechanisms. Trends Neurosci. 39, 74–85.

Bosman, C.A., Schoffelen, J.-M., Brunet, N., Oostenveld, R., Bastos, A.M., Womelsdorf, T., Rubehn, B., Stieglitz, T., De Weerd, P., and Fries, P. (2012). Attentional Stimulus Selection through Selective Synchronization between Monkey Visual Areas. Neuron 75, 875–888.

Bradshaw, J. (1967). Pupil Size as a Measure of Arousal during Information Processing. Nature 216, 515–516.

Brainard, D.H. (1997). The Psychophysics Toolbox. Spatial Vision 10, 433–436.

Bremmer, F., Klam, F., Duhamel, J.-R., Ben Hamed, S., and Graf, W. (2002). Visual-vestibular interactive responses in the macaque ventral intraparietal area (VIP). Eur. J. Neurosci. 16, 1569–1586.

Britten, K.H., Newsome, W.T., Shadlen, M.N., Celebrini, S., and Movshon, J.A. (1996). A relationship between behavioral choice and the visual responses of neurons in macaque MT. Visual Neuroscience 13, 87–100.

Bruce, C., Desimone, R., and Gross, C.G. (1981). Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. J. Neurophysiol. 46, 369–384.

197

Appendices

Brunton, B.W., Botvinick, M.M., and Brody, C.D. (2013). Rats and Humans Can Optimally Accumulate Evidence for Decision-Making. Science 340, 95–98.

Budinger, E., and Scheich, H. (2009). Anatomical connections suitable for the direct processing of neuronal information of different modalities via the rodent primary auditory cortex. Hearing Research 258, 16–27.

Burgess, C.P., Lak, A., Steinmetz, N.A., Zatka-Haas, P., Reddy, C.B., Jacobs, E.A.K., Linden, J.F., Paton, J.J., Ranson, A., Schröder, S., et al. (2017). High-Yield Methods for Accurate Two-Alternative Visual Psychophysics in Head-Fixed Mice. Cell Reports 20, 2513–2524.

Busse, L., Ayaz, A., Dhruv, N.T., Katzner, S., Saleem, A.B., Schölvinck, M.L., Zaharia, A.D., and Carandini, M. (2011). The Detection of Visual Contrast in the Behaving Mouse. J. Neurosci. 31, 11351–11361.

Bussey, T.J., Saksida, L.M., and Murray, E.A. (2002). Perirhinal cortex resolves feature ambiguity in complex visual discriminations. Eur. J. Neurosci. 15, 365–374.

Cafaro, J., and Rieke, F. (2010). Noise correlations improve response fidelity and stimulus encoding. Nature 468, 964–967.

Calvert, G.A., and Thesen, T. (2004). Multisensory integration: methodological approaches and emerging principles in the human brain. J. Physiol. Paris 98, 191–205.

Calvert, G.A., Bullmore, E.T., Brammer, M.J., Campbell, R., Williams, S.C., McGuire, P.K., Woodruff, P.W., Iversen, S.D., and David, A.S. (1997). Activation of auditory cortex during silent lipreading. Science 276, 593–596.

Campi, K.L., and Krubitzer, L. (2010). Comparative Studies of Diurnal and Nocturnal Rodents: Differences in

Lifestyle Result in Alterations in Cortical Field Size and Number. J Comp Neurol 518, 4491–4512.

Cappe, C., Rouiller, E.M., and Barone, P. (2009). Multisensory anatomical pathways. Hearing Research 258, 28–36.

Cappe, C., Murray, M.M., Barone, P., and Rouiller, E.M. (2010). Multisensory Facilitation of Behavior in Monkeys: Effects of Stimulus Intensity. Journal of Cognitive Neuroscience 22, 2850–2863.

Carandini, M., and Churchland, A.K. (2013). Probing perceptual decisions in rodents. Nat. Neurosci. 16, 824–831.

Carandini, M., and Heeger, D.J. (2012). Normalization as a canonical neural computation. Nat Rev Neurosci 13, 51–62.

Carandini, M., Heeger, D.J., and Movshon, J.A. (1997). Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex. J. Neurosci. 17, 8621–8644.

Carter-Dawson, L.D., and LaVail, M.M. (1979). Rods and cones in the mouse retina. I. Structural analysis using light and electron microscopy. J. Comp. Neurol. 188, 245–262.

Chelaru, M.I., and Dragoi, V. (2016). Negative Correlations in Visual Cortical Networks. Cereb Cortex 26, 246–256.

Chen, T.-W., Wardill, T.J., Sun, Y., Pulver, S.R., Renninger, S.L., Baohan, A., Schreiter, E.R., Kerr, R.A., Orger, M.B., Jayaraman, V., et al. (2013). Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300.

Chen, Y., Lin, C., Hsu, Y., and Hung, C.P. (2015). Network Anisotropy Trumps Noise for Efficient Object Coding in Macaque Inferior Temporal Cortex. J. Neurosci. 35, 9889–9899.

Cheng, A., Gonçalves, J., Golshani, P., Arisaka, K., and Portera-Cailliau, C. (2011). Simultaneous two-photon calcium

198

A

imaging at different depths with spatiotemporal multiplexing. Nat Met 8, 139–142.

Cohen, M.R., and Kohn, A. (2011). Measuring and interpreting neuronal correlations. Nat Neurosci 14, 811–819.

Cohen, M.R., and Maunsell, J.H.R. (2009). Attention improves performance primarily by reducing interneuronal correlations. Nat Neurosci 12, 1594–1600.

Colonius, H., and Diederich, A. (2004). Multisensory interaction in saccadic reaction time: a time-window-of-integration model. J Cogn Neurosci 16, 1000–1009.

Convento, S., Rahman, M.S., and Yau, J.M. (2018). Selective Attention Gates the Interactive Crossmodal Coupling between Perceptual Systems. Curr. Biol. 28, 746-752.e5.

Cotton, R.J., Froudarakis, E., Storer, P., Saggau, P., and Tolias, A.S. (2013). Three-dimensional mapping of microcircuit correlation structure. Front. Neural Circuits 7, 151.

Coull, J.T., Jones, M.E.P., Egan, T.D., Frith, C.D., and Maze, M. (2004). Attentional effects of noradrenaline vary with arousal level: selective activation of thalamic pulvinar in humans. NeuroImage 22, 315–322.

Cravo, A.M., Rohenkohl, G., Wyart, V., and Nobre, A.C. (2013). Temporal Expectation Enhances Contrast Sensitivity by Phase Entrainment of Low-Frequency Oscillations in Visual Cortex. J. Neurosci. 33, 4002–4010.

Crick, F., and Koch, C. (1995). Are we aware of neural activity in primary visual cortex? Nature 375, 121–123.

Cunningham, J.P., and Yu, B.M. (2014). Dimensionality reduction for large-scale neural recordings. Nat Neurosci 17, 1500–1509.

De Maesschalck, R., Jouan-Rimbaud, D., and Massart, D.L. (2000). The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems 50, 1–18.

De Meo, R., Murray, M.M., Clarke, S., and Matusz, P.J. (2015). Top-down control and early multisensory processes: chicken vs. egg. Front Integr Neurosci 9, 17.

Deneux, T., Kaszas, A., Szalay, G., Katona, G., Lakner, T., Grinvald, A., Rózsa, B., and Vanzetta, I. (2016). Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo. Nature Communications 7, 12190.

Deneux, T., Kempf, A., and Bathellier, B. (2018). Context-dependent signaling of coincident auditory and visual events in primary visual cortex. BioRxiv 258970.

Denève, S., and Machens, C.K. (2016). Efficient codes and balanced networks. Nat Neurosci 19, 375–382.

Deneve, S., Latham, P.E., and Pouget, A. (1999). Reading population codes: a neural implementation of ideal observers. Nat Neurosci 2, 740–745.

Denk, W., Strickler, J.H., and Webb, W.W. (1990). Two-photon laser scanning fluorescence microscopy. Science 248, 73–76.

Desimone, R., Albright, T.D., Gross, C.G., and Bruce, C. (1984). Stimulus-Selective Properties of Inferior Temporal Neurons in the Macaque. J. Neurosci. 4, 2051–2062.

DiCarlo, J.J., Zoccolan, D., and Rust, N.C. (2012). How Does the Brain Solve Visual Object Recognition? Neuron 73, 415–434.

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

199

Appendices

Diederich, A., and Colonius, H. (2008). Crossmodal interaction in saccadic reaction time: separating multisensory from warning effects in the time window of integration model. Exp Brain Res 186, 1–22.

Dokka, K., DeAngelis, G.C., and Angelaki, D.E. (2015). Multisensory Integration of Visual and Vestibular Signals Improves Heading Discrimination in the Presence of a Moving Object. J. Neurosci. 35, 13599–13607.

Dräger, U.C. (1975). Receptive fields of single cells and topography in mouse visual cortex. Journal of Comparative Neurology 160, 269–289.

Driver, J., and Noesselt, T. (2008). Multisensory Interplay Reveals Crossmodal Influences on ‘Sensory-Specific’ Brain Regions, Neural Responses, and Judgments. Neuron 57, 11–23.

Durand, S., Iyer, R., Mizuseki, K., Vries, S. de, Mihalas, S., and Reid, R.C. (2016). A Comparison of Visual Response Properties in the Lateral Geniculate Nucleus and Primary Visual Cortex of Awake and Anesthetized Mice. J. Neurosci. 36, 12144–12156.

Ecker, A.S., Berens, P., Keliris, G.A., Bethge, M., Logothetis, N.K., and Tolias, A.S. (2010). Decorrelated Neuronal Firing in Cortical Microcircuits. Science 327, 584–587.

Ecker, A.S., Berens, P., Tolias, A.S., and Bethge, M. (2011). The Effect of Noise Correlations in Populations of Diversely Tuned Neurons. J. Neurosci. 31, 14272–14283.

Ehret, R. (1997). The Central Auditory System (Oxford University Press).

Eichhorn, J., Sinz, F., and Bethge, M. (2009). Natural Image Coding in V1: How Much Use Is Orientation Selectivity? PLoS Comput Biol 5, e1000336.

Ernst, M.O., and Banks, M.S. (2002). Humans integrate visual and haptic information in a statistically optimal fashion. Nature

415, 429–433.

Ernst, M.O., and Bülthoff, H.H. (2004). Merging the senses into a robust percept. Trends in Cognitive Sciences 8, 162–169.

Faisal, A.A., Selen, L.P.J., and Wolpert, D.M. (2008). Noise in the nervous system. Nat Rev Neurosci 9, 292–303.

Faivre, N., Berthet, V., and Kouider, S. (2014). Sustained invisibility through crowding and continuous flash suppression: a comparative review. Front Psychol 5, 475.

Falchier, A., Clavagnier, S., Barone, P., and Kennedy, H. (2002). Anatomical Evidence of Multimodal Integration in Primate Striate Cortex. J. Neurosci. 22, 5749–5759.

Felleman, D.J., and Van Essen, D.C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1, 1–47.

Feng, W., Störmer, V.S., Martinez, A., McDonald, J.J., and Hillyard, S.A. (2014). Sounds activate visual cortex and improve visual discrimination. J. Neurosci. 34, 9817–9824.

Fetsch, C.R., Turner, A.H., DeAngelis, G.C., and Angelaki, D.E. (2009). Dynamic reweighting of visual and vestibular cues during self-motion perception. J. Neurosci. 29, 15601–15612.

Fetsch, C.R., Pouget, A., DeAngelis, G.C., and Angelaki, D.E. (2012). Neural correlates of reliability-based cue weighting during multisensory integration. Nat Neurosci 15, 146–154.

Fetsch, C.R., DeAngelis, G.C., and Angelaki, D.E. (2013). Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons. Nature Reviews Neuroscience 14, 429–442.

Fiscella, M., Franke, F., Farrow, K., Müller, J., Roska, B., Silveira, R.A. da, and Hierlemann, A. (2015). Visual coding

200

A

with a population of direction-selective neurons. Journal of Neurophysiology 114, 2485–2499.

Forster, B., Cavina-Pratesi, C., Aglioti, S.M., and Berlucchi, G. (2002). Redundant target effect and intersensory facilitation from visual-tactile interactions in simple reaction time. Exp Brain Res 143, 480–487.

Foxworthy, W.A., Allman, B.L., Keniston, L.P., and Meredith, M.A. (2013a). Multisensory and unisensory neurons in ferret parietal cortex exhibit distinct functional properties. Eur. J. Neurosci. 37, 910–923.

Foxworthy, W.A., Clemo, H.R., and Meredith, M.A. (2013b). Laminar and connectional organization of a multisensory cortex. J. Comp. Neurol. 521, 1867–1890.

Franke, F., Fiscella, M., Sevelev, M., Roska, B., Hierlemann, A., and Azeredo da Silveira, R. (2016). Structures of Neural Correlation and How They Favor Coding. Neuron 89, 409–422.

Fried, I., MacDonald, K.A., and Wilson, C.L. (1997). Single Neuron Activity in Human Hippocampus and Amygdala during Recognition of Faces and Objects. Neuron 18, 753–765.

Fries, P. (2015). Rhythms for Cognition: Communication through Coherence. Neuron 88, 220–235.

Friston, K. (2010). The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138.

Froudarakis, E., Berens, P., Ecker, A.S., Cotton, R.J., Sinz, F.H., Yatsenko, D., Saggau, P., Bethge, M., and Tolias, A.S. (2014). Population code in mouse V1 facilitates readout of natural scenes through increased sparseness. Nat Neurosci 17, 851–857.

de Gelder, B., Vroomen, J., de Jong, S.J., Masthoff, E.D., Trompenaars, F.J., and Hodiamont, P. (2005). Multisensory integration of emotional faces and voices

in schizophrenics. Schizophr. Res. 72, 195–203.

Gentet, L.J. (2012). Functional diversity of supragranular GABAergic neurons in the barrel cortex. Front. Neural Circuits 6, 52.

Ghazanfar, A.A., and Lemus, L. (2010). Multisensory integration: vision boosts information through suppression in auditory cortex. Curr. Biol. 20, R22-23.

Ghazanfar, A.A., and Schroeder, C.E. (2006). Is neocortex essentially multisensory? Trends in Cognitive Sciences 10, 278–285.

Ghazanfar, A.A., Maier, J.X., Hoffman, K.L., and Logothetis, N.K. (2005). Multisensory Integration of Dynamic Faces and Voices in Rhesus Monkey Auditory Cortex. J. Neurosci. 25, 5004–5012.

Gielen, S.C., Schmidt, R.A., and Van den Heuvel, P.J. (1983). On the nature of intersensory facilitation of reaction time. Percept Psychophys 34, 161–168.

Gifford, G.W., and Cohen, Y.E. (2004). Effect of a central fixation light on auditory spatial responses in area LIP. J. Neurophysiol. 91, 2929–2933.

Gingras, G., Rowland, B.A., and Stein, B.E. (2009). The Differing Impact of Multisensory and Unisensory Integration on Behavior. J. Neurosci. 29, 4897–4902.

Gleiss, S., and Kayser, C. (2012). Audio-Visual Detection Benefits in the Rat. PLoS ONE 7, e45677.

Gleiss, S., and Kayser, C. (2013). Acoustic Noise Improves Visual Perception and Modulates Occipital Oscillatory States. Journal of Cognitive Neuroscience 26, 699–711.

Glickfeld, L.L., and Olsen, S.R. (2017). Higher-Order Areas of the Mouse Visual Cortex. Annual Review of Vision Science 3, null.

201

Appendices

Glickfeld, L.L., Andermann, M.L., Bonin, V., and Reid, R.C. (2013a). Cortico-cortical projections in mouse visual cortex are functionally target specific. Nature Neuroscience 16, 219–226.

Glickfeld, L.L., Histed, M.H., and Maunsell, J.H.R. (2013b). Mouse Primary Visual Cortex Is Used to Detect Both Orientation and Contrast Changes. J. Neurosci. 33, 19416–19422.

Glickfeld, L.L., Reid, R.C., and Andermann, M.L. (2014). A mouse model of higher visual cortical function. Current Opinion in Neurobiology 24, 28–33.

Goldey, G.J., Roumis, D.K., Glickfeld, L.L., Kerlin, A.M., Reid, R.C., Bonin, V., Schafer, D.P., and Andermann, M.L. (2014). Removable cranial windows for long-term imaging in awake mice. Nat. Protocols 9, 2515–2538.

Goldstein, M.H., Hall, J.L., and Butterfield, B.O. (1968). Single‐Unit Activity in the Primary Auditory Cortex of Unanesthetized Cats. The Journal of the Acoustical Society of America 43, 444–455.

Goltstein, P.M., Coffey, E.B.J., Roelfsema, P.R., and Pennartz, C.M.A. (2013). In Vivo Two-Photon Ca2+ Imaging Reveals Selective Reward Effects on Stimulus-Specific Assemblies in Mouse Visual Cortex. J. Neurosci. 33, 11540–11555.

Goltstein, P.M., Montijn, J.S., and Pennartz, C.M.A. (2015). Effects of Isoflurane Anesthesia on Ensemble Patterns of Ca 2+ Activity in Mouse V1: Reduced Direction Selectivity Independent of Increased Correlations in Cellular Activity. PLOS ONE 10, e0118277.

Goltstein, P.M., Meijer, G.T., and Pennartz, C.M. (2018). Conditioning sharpens the spatial representation of rewarded stimuli in mouse primary visual cortex. Elife 7, e37683

Gondan, M., Niederhaus, B., Rösler, F., and Röder, B. (2005). Multisensory

processing in the redundant-target effect: A behavioral and event-related potential study. Perception & Psychophysics 67, 713–726.

Göppert-Mayer, M. (1931). Über Elementarakte mit zwei Quantensprüngen. Annalen Der Physik 401, 273–294.

Greenberg, D.S., Houweling, A.R., and Kerr, J.N.D. (2008). Population imaging of ongoing neuronal activity in the visual cortex of awake rats. Nat Neurosci 11, 749–751.

Gregory, R.L. (1980). Perceptions as hypotheses. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 290, 181–197.

Gu, Y., Angelaki, D.E., and DeAngelis, G.C. (2008). Neural correlates of multisensory cue integration in macaque MSTd. Nat Neurosci 11, 1201–1210.

Gu, Y., Deangelis, G.C., and Angelaki, D.E. (2012). Causal links between dorsal medial superior temporal area neurons and multisensory heading perception. J. Neurosci. 32, 2299–2313.

Gu, Y., Cheng, Z., Yang, L., DeAngelis, G.C., and Angelaki, D.E. (2016). Multisensory Convergence of Visual and Vestibular Heading Cues in the Pursuit Area of the Frontal Eye Field. Cereb. Cortex 26, 3785–3801.

Guizar-Sicairos, M., Thurman, S.T., and Fienup, J.R. (2008). Efficient subpixel image registration algorithms. Opt Lett 33, 156–158.

Guo, Z.V., Inagaki, H.K., Daie, K., Druckmann, S., Gerfen, C.R., and Svoboda, K. (2017). Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186.

Hammond-Kenny, A., Bajo, V.M., King, A.J., and Nodal, F.R. (2017). Behavioural benefits of multisensory processing in ferrets. Eur J Neurosci 45, 278–289.

Hanks, T.D., Kopec, C.D., Brunton, B.W., Duan, C.A., Erlich, J.C., and Brody, C.D.

202

A

(2015). Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223.

Hansen, B.J., Chelaru, M.I., and Dragoi, V. (2012). Correlated Variability in Laminar Cortical Circuits. Neuron 76, 590–602.

Harris, K. (2005). Neural signatures of cell assembly organization. Nat Rev Neurosci 6, 399–407.

Harvey, C.D., Coen, P., and Tank, D.W. (2012). Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68.

Helmholtz, H. von (1867). Handbuch der physiologischen Optik (Voss).

Henry, K.R., and Chole, R.A. (1980). Genotypic Differences in Behavioral, Physiological and Anatomical Expressions of Age-Related Hearing Loss in the Laboratory Mouse. Audiology 19, 369–383.

Herrero, J.L., Gieselmann, M.A., Sanayei, M., and Thiele, A. (2013). Attention-Induced Variance and Noise Correlation Reduction in Macaque V1 Is Mediated by NMDA Receptors. Neuron 78, 729–739.

Hirokawa, J., Bosch, M., Sakata, S., Sakurai, Y., and Yamamori, T. (2008). Functional role of the secondary visual cortex in multisensory facilitation in rats. Neuroscience 153, 1402–1417.

Hirokawa, J., Sadakane, O., Sakata, S., Bosch, M., Sakurai, Y., and Yamamori, T. (2011). Multisensory Information Facilitates Reaction Speed by Enlarging Activity Difference between Superior Colliculus Hemispheres in Rats. PLoS ONE 6, e25283.

Hishida, R., Kudoh, M., and Shibuki, K. (2014). Multimodal cortical sensory pathways revealed by sequential transcranial electrical stimulation in mice. Neuroscience Research 87, 49–55.

Histed, M.H., Carvalho, L.A., and Maunsell, J.H.R. (2012). Psychophysical measurement of contrast sensitivity in

the behaving mouse. J Neurophysiol 107, 758–765.

Hofbauer, A., and Dräger, U.C. (2004). Depth segregation of retinal ganglion cells projecting to mouse superior colliculus. Journal of Comparative Neurology 234, 465–474.

Hofstetter, K.M., and Ehret, G. (1992). The auditory cortex of the mouse: Connections of the ultrasonic field. The Journal of Comparative Neurology 323, 370–386.

Hollensteiner, K.J., Pieper, F., Engler, G., König, P., and Engel, A.K. (2015). Crossmodal Integration Improves Sensory Detection Thresholds in the Ferret. PLOS ONE 10, e0124952.

Holmes, N.P. (2007). The law of inverse effectiveness in neurons and behaviour: Multisensory integration versus normal variability. Neuropsychologia 45, 3340–3345.

Holmes, N.P. (2009). The Principle of Inverse Effectiveness in Multisensory Integration: Some Statistical Considerations. Brain Topogr 21, 168–176.

Hubel, D.H. (1982). Exploration of the primary visual cortex, 1955–78. Nature 299, 515–524.

Hubel, D.H., and Livingstone, M.S. (1987). Segregation of Form, Color, and Stereopsis in Primate Area 18. J. Neurosci. 7, 3378–3415.

Hubel, D.H., and Wiesel, T.N. (1959). Receptive fields of single neurons in the cat’s striate cortex. The Journal of Physiology 148, 574–591.

Hubel, D.H., and Wiesel, T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 160, 106–154.

203

Appendices

Hubel, D.H., and Wiesel, T.N. (1968). Receptive Fields and Functional Architecture of Monkey Striate Cortex. J Physiol 195, 215–243.

Hübener, M. (2003). Mouse visual cortex. Current Opinion in Neurobiology 13, 413–420.

Hwang, J., and Romanski, L.M. (2015). Prefrontal Neuronal Responses during Audiovisual Mnemonic Processing. J. Neurosci. 35, 960–971.

Ibrahim, L.A., Mesik, L., Ji, X., Fang, Q., Li, H., Li, Y., Zingg, B., Zhang, L.I., and Tao, H.W. (2016). Cross-Modality Sharpening of Visual Cortical Processing through Layer-1-Mediated Inhibition and Disinhibition. Neuron 89, 1031–1045.

Ince, R.A.A., Panzeri, S., and Kayser, C. (2013). Neural Codes Formed by Small and Temporally Precise Populations in Auditory Cortex. J. Neurosci. 33, 18277–18287.

Iurilli, G., Ghezzi, D., Olcese, U., Lassi, G., Nazzaro, C., Tonini, R., Tucci, V., Benfenati, F., and Medini, P. (2012). Sound-Driven Synaptic Inhibition in Primary Visual Cortex. Neuron 73, 814–828.

Jackendoff, R.S. (1987). Consciousness and the Computational Mind (MIT Press).

Jacklin, D.L., Cloke, J.M., Potvin, A., Garrett, I., and Winters, B.D. (2016). The Dynamic Multisensory Engram: Neural Circuitry Underlying Crossmodal Object Recognition in Rats Changes with the Nature of Object Experience. J. Neurosci. 36, 1273–1289.

Javitt, D.C. (2009). Sensory processing in schizophrenia: neither simple nor intact. Schizophr Bull 35, 1059–1064.

Jazayeri, M., and Movshon, J.A. (2006). Optimal representation of sensory information by neural populations. Nat Neurosci 9, 690–696.

Jiang, W., Wallace, M.T., Jiang, H., Vaughan, J.W., and Stein, B.E. (2001). Two cortical areas mediate multisensory integration in superior colliculus neurons. J. Neurophysiol. 85, 506–522.

Jiang, W., Jiang, H., and Stein, B.E. (2002). Two corticotectal areas facilitate multisensory orientation behavior. J Cogn Neurosci 14, 1240–1255.

Jones, P.R. (2016). A tutorial on cue combination and Signal Detection Theory: Using changes in sensitivity to evaluate how observers integrate sensory information. Journal of Mathematical Psychology 73, 117–139.

Kaas, J.H. (1980). A Comparative Survey of Visual Cortex Organization in Mammals. In Comparative Neurology of the Telencephalon, (Springer, Boston, MA), pp. 483–502.

Kadunce, D.C., Vaughan, J.W., Wallace, M.T., and Stein, B.E. (2001). The influence of visual and auditory receptive field organization on multisensory integration in the superior colliculus. Exp Brain Res 139, 303–310.

Kanitscheider, I., Coen-Cagli, R., and Pouget, A. (2015). Origin of information-limiting noise correlations. PNAS 112, E6973–E6982.

Kayser, C., and Logothetis, N.K. (2007). Do early sensory cortices integrate cross-modal information? Brain Struct Funct 212, 121–132.

Kayser, C., Salazar, R.F., and König, P. (2003). Responses to Natural Scenes in Cat V1. Journal of Neurophysiology 90, 1910–1920.

Kayser, C., Petkov, C.I., and Logothetis, N.K. (2008). Visual Modulation of Neurons in Auditory Cortex. Cereb. Cortex 18, 1560–1574.

Kayser, C., Logothetis, N.K., and Panzeri, S. (2010). Visual Enhancement of the Information Representation in Auditory Cortex. Current Biology 20, 19–24.

204

A

van Kemenade, B.M., Seymour, K., Wacker, E., Spitzer, B., Blankenburg, F., and Sterzer, P. (2014). Tactile and visual motion direction processing in hMT+/V5. Neuroimage 84, 420–427.

Kerr, J.N.D., and Denk, W. (2008). Imaging in vivo: watching the brain in action. Nat Rev Neurosci 9, 195–205.

Khastkhodaei, Z., Jurjut, O., Katzner, S., and Busse, L. (2016). Mice Can Use Second-Order, Contrast-Modulated Stimuli to Guide Visual Perception. J. Neurosci. 36, 4457–4469.

Knight, B.W. (1972). The Relationship between the Firing Rate of a Single Neuron and the Level of Activity in a Population of Neurons: Experimental evidence for resonant enhancement in the population response. The Journal of General Physiology 59, 767–778.

Knill, D.C., and Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences 27, 712–719.

Knutson, B., Burgdorf, J., and Panksepp, J. (2002). Ultrasonic vocalizations as indices of affective states in rats. Psychol Bull 128, 961–977.

Kohn, A., and Smith, M.A. (2005). Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque. J. Neurosci. 25, 3661–3673.

Kontsevich, L.L., and Tyler, C.W. (1999). Bayesian adaptive estimation of psychometric slope and threshold. Vision Research 39, 2729–2737.

Kwon, S.E., Yang, H., Minamisawa, G., and O’Connor, D.H. (2016). Sensory and decision-related activity propagate in a cortical feedback loop during touch perception. Nature Neuroscience 19, 1243.

Lakatos, P., Karmos, G., Mehta, A.D., Ulbert, I., and Schroeder, C.E. (2008). Entrainment of Neuronal Oscillations as a Mechanism of Attentional Selection. Science 320,

110–113.

Laramée, M.E., Kurotani, T., Rockland, K.S., Bronchti, G., and Boire, D. (2011). Indirect pathway between the primary auditory and visual cortices through layer V pyramidal neurons in V2L in mouse and the effects of bilateral enucleation. European Journal of Neuroscience 34, 65–78.

Latham, P.E., Deneve, S., and Pouget, A. (2003). Optimal computation with attractor networks. Journal of Physiology-Paris 97, 683–694.

Laurienti, P.J., Kraft, R.A., Maldjian, J.A., Burdette, J.H., and Wallace, M.T. (2004). Semantic congruence is a critical factor in multisensory behavioral performance. Experimental Brain Research 158, 405–414.

Lee, J., and Maunsell, J.H.R. (2009). A Normalization Model of Attentional Modulation of Single Unit Responses. PLoS ONE 4, e4651.

Lee, C., Rohrer, W.H., and Sparks, D.L. (1988). Population coding of saccadic eye movements by neurons in the superior colliculus. Nature 332, 357–360.

Lee, C.C.Y., Diamond, M.E., and Arabzadeh, E. (2016). Sensory Prioritization in Rats: Behavioral Performance and Neuronal Correlates. J. Neurosci. 36, 3243–3253.

Lee, D., Port, N.L., Kruse, W., and Georgopoulos, A.P. (1998). Variability and Correlated Noise in the Discharge of Neurons in Motor and Parietal Areas of the Primate Cortex. J. Neurosci. 18, 1161–1170.

Lehmann, S., and Murray, M.M. (2005). The role of multisensory memories in unisensory object discrimination. Brain Res Cogn Brain Res 24, 326–334.

Lemus, L., Hernández, A., Luna, R., Zainos, A., and Romo, R. (2010). Do Sensory Cortices Process More than One Sensory Modality during Perceptual Judgments? Neuron 67, 335–348.

205

Appendices

Leo, F., Romei, V., Freeman, E., Ladavas, E., and Driver, J. (2011). Looming sounds enhance orientation sensitivity for visual stimuli on the same side as such sounds. Exp Brain Res 213, 193–201.

Licata, A.M., Kaufman, M.T., Raposo, D., Ryan, M.B., Sheppard, J.P., and Churchland, A.K. (2017). Posterior Parietal Cortex Guides Visual Decisions in Rats. J. Neurosci. 37, 4954–4966.

Limanowski, J., and Blankenburg, F. (2016). Integration of Visual and Proprioceptive Limb Position Information in Human Posterior Parietal, Premotor, and Extrastriate Cortex. J. Neurosci. 36, 2582–2589.

Lippert, M., Logothetis, N.K., and Kayser, C. (2007). Improvement of visual contrast detection by a simultaneous sound. Brain Research 1173, 102–109.

Lippert, M.T., Takagaki, K., Kayser, C., and Ohl, F.W. (2013). Asymmetric multisensory interactions of visual and somatosensory responses in a region of the rat parietal cortex. PLoS ONE 8, e63631.

Lütcke, H., Margolis, D.J., and Helmchen, F. (2013). Steady or changing? Long-term monitoring of neuronal population activity. Trends in Neurosciences 36, 375–384.

Ma, W.J., Beck, J.M., Latham, P.E., and Pouget, A. (2006). Bayesian inference with probabilistic population codes. Nat Neurosci 9, 1432–1438.

Macaluso, E. (2006). Multisensory Processing in Sensory-Specific Cortical Areas. Neuroscientist 12, 327–338.

Maier, J.X., Blankenship, M.L., Li, J.X., and Katz, D.B. (2015). A Multisensory Network for Olfactory Processing. Curr. Biol. 25, 2642–2650.

Mank, M., Santos, A.F., Direnberger, S., Mrsic-Flogel, T.D., Hofer, S.B., Stein, V., Hendel, T., Reiff, D.F., Levelt, C., Borst, A., et al. (2008). A genetically encoded calcium indicator for chronic in vivo two-

photon imaging. Nat Meth 5, 805–811.

Marcel, A.J. (1983). Conscious and unconscious perception: an approach to the relations between phenomenal experience and perceptual processes. Cogn Psychol 15, 238–300.

Marco, E.J., Hinkley, L.B.N., Hill, S.S., and Nagarajan, S.S. (2011). Sensory processing in autism: a review of neurophysiologic findings. Pediatr. Res. 69, 48R-54R.

Marshel, J.H., Garrett, M.E., Nauhaus, I., and Callaway, E.M. (2011). Functional Specialization of Seven Mouse Visual Cortical Areas. Neuron 72, 1040–1054.

Masland, R.H. (2001). Neuronal diversity in the retina. Current Opinion in Neurobiology 11, 431–436.

Matusz, P.J., Wallace, M.T., and Murray, M.M. (2017). A multisensory perspective on object memory. Neuropsychologia 105, 243–252.

McGinley, M.J., David, S.V., and McCormick, D.A. (2015a). Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection. Neuron 87, 179–192.

McGinley, M.J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C.R., Tolias, A.S., Cardin, J.A., and McCormick, D.A. (2015b). Waking State: Rapid Variations Modulate Neural and Behavioral Responses. Neuron 87, 1143–1161.

Meijer, G.T., Montijn, J.S., Pennartz, C.M.A., and Lansink, C.S. (2017). Audiovisual Modulation in Mouse Primary Visual Cortex Depends on Cross-Modal Stimulus Configuration and Congruency. J. Neurosci. 37, 8783–8796.

Meijer, G.T., Pie, J.L., Dolman, T.L., Pennartz, C.M.A., and Lansink, C.S. (2018). Audiovisual Integration Enhances Stimulus Detection Performance in Mice. Front. Behav. Neurosci. 12.

206

A

Meredith, M.A., and Stein, B.E. (1983). Interactions among converging sensory inputs in the superior colliculus. Science 221, 389–391.

Meredith, M.A., and Stein, B.E. (1986a). Visual, auditory, and somatosensory convergence on cells in superior colliculus results in multisensory integration. J Neurophysiol 56, 640–662.

Meredith, M.A., and Stein, B.E. (1986b). Spatial factors determine the activity of multisensory neurons in cat superior colliculus. Brain Res. 365, 350–354.

Meredith, M.A., and Stein, B.E. (1986c). Visual, auditory, and somatosensory convergence on cells in superior colliculus results in multisensory integration. J. Neurophysiol. 56, 640–662.

Meredith, M.A., Nemitz, J.W., and Stein, B.E. (1987). Determinants of multisensory integration in superior colliculus neurons. I. Temporal factors. J. Neurosci. 7, 3215–3229.

Meyniel, F., Sigman, M., and Mainen, Z.F. (2015). Confidence as Bayesian Probability: From Neural Origins to Behavior. Neuron 88, 78–92.

Miller, J. (1982). Divided attention: Evidence for coactivation with redundant signals. Cognitive Psychology 14, 247–279.

Miller, J. (1986). Timecourse of coactivation in bimodal divided attention. Percept Psychophys 40, 331–343.

Miller, M.W., and Vogt, B.A. (1984). Direct connections of rat visual cortex with sensory, motor, and association cortices. J. Comp. Neurol. 226, 184–202.

Miller, J.K., Ayzenshtat, I., Carrillo-Reid, L., and Yuste, R. (2014). Visual stimuli recruit intrinsically generated cortical ensembles. PNAS 111, E4053–E4061.

Mohan, H., de Haan, R., Mansvelder, H.D., and de Kock, C.P.J. (2017). The posterior parietal cortex as integrative hub for whisker sensorimotor information.

Neuroscience.

Molholm, S., Ritter, W., Javitt, D.C., and Foxe, J.J. (2004). Multisensory visual-auditory object recognition in humans: a high-density electrical mapping study. Cereb. Cortex 14, 452–465.

Montijn, J.S. (2016). A journey through neural space: Neuronal population correlates of visual processing and perception (OisterwijkUitgeverij BOXPress9789462954748).

Montijn, J.S., Klink, P.C., and van Wezel, R.J.A. (2012). Divisive Normalization and Neuronal Oscillations in a Single Hierarchical Framework of Selective Visual Attention. Front Neural Circuits 6.

Montijn, J.S., Vinck, M., and Pennartz, C.M.A. (2014). Population coding in mouse visual cortex: response reliability and dissociability of stimulus tuning and noise correlation. Front. Comput. Neurosci 8, 58.

Montijn, J.S., Goltstein, P.M., and Pennartz, C.M. (2015). Mouse V1 population correlates of visual detection rely on heterogeneity within neuronal response patterns. ELife 4, e10163.

Montijn, J.S., Meijer, G.T., Lansink, C.S., and Pennartz, C.M.A. (2016a). Population-Level Neural Codes Are Robust to Single-Neuron Variability from a Multidimensional Coding Perspective. Cell Reports 16, 2486–2498.

Montijn, J.S., Olcese, U., and Pennartz, C.M.A. (2016b). Visual Stimulus Detection Correlates with the Consistency of Temporal Sequences within Stereotyped Events of V1 Neuronal Population Activity. J. Neurosci. 36, 8624–8640.

Moran, Z.D., Bachman, P., Pham, P., Cho, S.H., Cannon, T.D., and Shams, L. (2013). Multisensory encoding improves auditory recognition. Multisens Res 26, 581–592.

207

Appendices

Moreno-Bote, R., Beck, J., Kanitscheider, I., Pitkow, X., Latham, P., and Pouget, A. (2014). Information-limiting correlations. Nat Neurosci advance online publication.

Morgan, M.L., DeAngelis, G.C., and Angelaki, D.E. (2008). Multisensory Integration in Macaque Visual Cortex Depends on Cue Reliability. Neuron 59, 662–673.

Mudrik, L., Faivre, N., and Koch, C. (2014). Information integration without awareness. Trends Cogn. Sci. (Regul. Ed.) 18, 488–496.

Mumford, D. (1992). On the computational architecture of the neocortex. II. The role of cortico-cortical loops. Biol Cybern 66, 241–251.

Murray, M.M., and Wallace, M.T. (2011). The Neural Bases of Multisensory Processes (CRC Press).

Murray, M.M., Thelen, A., Thut, G., Romei, V., Martuzzi, R., and Matusz, P.J. (2016a). The multisensory function of the human primary visual cortex. Neuropsychologia 83, 161–169.

Murray, M.M., Lewkowicz, D.J., Amedi, A., and Wallace, M.T. (2016b). Multisensory Processes: A Balancing Act across the Lifespan. Trends Neurosci. 39, 567–579.

Niell, C.M., and Stryker, M.P. (2008). Highly Selective Receptive Fields in Mouse Visual Cortex. J. Neurosci. 28, 7520–7536.

Nikbakht, N., Tafreshiha, A., Zoccolan, D., and Diamond, M.E. (2018). Supralinear and Supramodal Integration of Visual and Tactile Signals in Rats: Psychophysics and Neuronal Mechanisms. Neuron 97, 626-639.

Nitz, D.A. (2006). Tracking route progression in the posterior parietal cortex. Neuron 49, 747–756.

Oever, S. ten, Schroeder, C.E., Poeppel, D., Atteveldt, N. van, Mehta, A.D., Mégevand, P., Groppe, D.M., and Zion-Golumbic, E. (2017). Low-Frequency Cortical

Oscillations Entrain to Subthreshold Rhythmic Auditory Stimuli. J. Neurosci. 37, 4903–4912.

ten Oever, S., Schroeder, C.E., Poeppel, D., van Atteveldt, N., and Zion-Golumbic, E. (2014). Rhythmicity and cross-modal temporal cues facilitate detection. Neuropsychologia 63, 43–50.

ten Oever, S., Romei, V., van Atteveldt, N., Soto-Faraco, S., Murray, M.M., and Matusz, P.J. (2016). The COGs (context, object, and goals) in multisensory processing. Exp Brain Res 234, 1307–1323.

Ohki, K., Chung, S., Ch’ng, Y.H., Kara, P., and Reid, R.C. (2005). Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature 433, 597–603.

Ohshiro, T., Angelaki, D.E., and DeAngelis, G.C. (2011). A Normalization Model of Multisensory Integration. Nat Neurosci 14, 775–782.

Ohshiro, T., Angelaki, D.E., and DeAngelis, G.C. (2017). A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex. Neuron 95, 399-411.e8.

Olcese, U., Iurilli, G., and Medini, P. (2013). Cellular and Synaptic Architecture of Multisensory Integration in the Mouse Neocortex. Neuron 79, 579–593.

Pachitariu, M., Stringer, C., Schröder, S., Dipoppa, M., Rossi, L.F., Carandini, M., and Harris, K.D. (2016). Suite2p: beyond 10,000 neurons with standard two-photon microscopy. BioRxiv 061507.

Paperna, T., and Malach, R. (1991). Patterns of sensory intermodality relationships in the cerebral cortex of the rat. J. Comp. Neurol. 308, 432–456.

Park, I.M., Meister, M.L.R., Huk, A.C., and Pillow, J.W. (2014). Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat Neurosci 17, 1395–1403.

208

A

Parker, A.J., and Newsome, W.T. (1998). SENSE AND THE SINGLE NEURON: Probing the Physiology of Perception. Annual Review of Neuroscience 21, 227–277.

Pasupathy, A., and Connor, C.E. (2002). Population coding of shape in area V4. Nat Neurosci 5, 1332–1338.

Paxinos, G., and Franklin, K.B.J. (2004). The Mouse Brain in Stereotaxic Coordinates (Elsevier/Academic Press).

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825−2830.

Pelli, D.G. (1997). The VideoToolbox software for visual psychophysics: Transforming numbers into movies. Spatial Vision 10, 437–442.

Pennartz, C.M.A. (2009). Identification and integration of sensory modalities: neural basis and relation to consciousness. Conscious Cogn 18, 718–739.

Pennartz, C.M.A. (2015). The Brain’s Representational Power (MIT Press).

Pennartz, C.M.A. (2018). Consciousness, Representation, Action: The Importance of Being Goal-Directed. Trends Cogn. Sci. (Regul. Ed.) 22, 137–153.

Peron, S.P., Freeman, J., Iyer, V., Guo, C., and Svoboda, K. (2015). A Cellular Resolution Map of Barrel Cortex Activity during Tactile Behavior. Neuron 86, 783–799.

Perrault, T.J., Vaughan, J.W., Stein, B.E., and Wallace, M.T. (2003). Neuron-Specific Response Characteristics Predict the Magnitude of Multisensory Integration. Journal of Neurophysiology 90, 4022–4026.

Peters, A.J., Chen, S.X., and Komiyama, T. (2014). Emergence of reproducible spatiotemporal activity during motor

learning. Nature 510, 263–267.

Pfeffer, C.K., Xue, M., He, M., Huang, Z.J., and Scanziani, M. (2013). Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat Neurosci 16, 1068–1076.

Pillow, J.W., Shlens, J., Paninski, L., Sher, A., Litke, A.M., Chichilnisky, E.J., and Simoncelli, E.P. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999.

Pitkow, X., Liu, S., Angelaki, D.E., DeAngelis, G.C., and Pouget, A. (2015). How Can Single Sensory Neurons Predict Behavior? Neuron 87, 411–423.

Platisa, J., and Pieribone, V.A. (2018). Genetically encoded fluorescent voltage indicators: are we there yet? Current Opinion in Neurobiology 50, 146–153.

Pnevmatikakis, E.A., Soudry, D., Gao, Y., Machado, T.A., Merel, J., Pfau, D., Reardon, T., Mu, Y., Lacefield, C., Yang, W., et al. (2016). Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data. Neuron 89, 285–299.

Poort, J., Khan, A.G., Pachitariu, M., Nemri, A., Orsolic, I., Krupic, J., Bauza, M., Sahani, M., Keller, G.B., Mrsic-Flogel, T.D., et al. (2015). Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex. Neuron 86, 1478–1490.

Portfors, C.V. (2007). Types and functions of ultrasonic vocalizations in laboratory rats and mice. J. Am. Assoc. Lab. Anim. Sci. 46, 28–34.

Pouget, A., Deneve, S., and Duhamel, J.-R. (2002). A computational perspective on the neural basis of multisensory spatial representations. Nat. Rev. Neurosci. 3, 741–747.

209

Appendices

Quiroga, R.Q., Reddy, L., Kreiman, G., Koch, C., and Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107.

Raab, D.H. (1962). Statistical facilitation of simple reaction times. Trans N Y Acad Sci 24, 574–590.

Raposo, D., Sheppard, J.P., Schrater, P.R., and Churchland, A.K. (2012). Multisensory Decision-Making in Rats and Humans. Journal of Neuroscience 32, 3726–3735.

Raposo, D., Kaufman, M.T., and Churchland, A.K. (2014). A category-free neural population supports evolving demands during decision-making. Nature Neuroscience 17, 1784–1792.

Regenbogen, C., Johansson, E., Andersson, P., Olsson, M.J., and Lundström, J.N. (2016). Bayesian-based integration of multisensory naturalistic perithreshold stimuli. Neuropsychologia 88, 123–130.

Reynolds, J.H., and Heeger, D.J. (2009). The Normalization Model of Attention. Neuron 61, 168–185.

Ringach, D.L. (2010). Population coding under normalization. Vision Res. 50, 2223–2232.

Rose, T., Goltstein, P.M., Portugues, R., and Griesbeck, O. (2014). Putting a finishing touch on GECIs. Front Mol Neurosci 7.

Ross, L.A., Saint-Amour, D., Leavitt, V.M., Molholm, S., Javitt, D.C., and Foxe, J.J. (2007). Impaired multisensory processing in schizophrenia: deficits in the visual enhancement of speech comprehension under noisy environmental conditions. Schizophr. Res. 97, 173–183.

Rossi-Pool, R., Salinas, E., Zainos, A., Alvarez, M., Vergara, J., Parga, N., and Romo, R. (2016). Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli. Proc. Natl. Acad. Sci. U.S.A. 113, E7966–E7975.

Rouger, J., Lagleyre, S., Fraysse, B., Deneve, S., Deguine, O., and Barone, P. (2007). Evidence that cochlear-implanted deaf patients are better multisensory integrators. PNAS 104, 7295–7300.

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

Roxin, A., Brunel, N., Hansel, D., Mongillo, G., and Vreeswijk, C. van (2011). On the Distribution of Firing Rates in Networks of Cortical Neurons. J. Neurosci. 31, 16217–16226.

Schneidman, E., Berry, M.J., Segev, R., and Bialek, W. (2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440, 1007–1012.

Schölvinck, M.L., Saleem, A.B., Benucci, A., Harris, K.D., and Carandini, M. (2015). Cortical State Determines Global Variability and Correlations in Visual Cortex. J. Neurosci. 35, 170–178.

Schroeder, C.E., and Foxe, J. (2005). Multisensory contributions to low-level, ‘unisensory’ processing. Current Opinion in Neurobiology 15, 454–458.

Schroeder, C.E., and Lakatos, P. (2009). Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci. 32, 9–18.

Seitz, A.R., Kim, R., and Shams, L. (2006). Sound facilitates visual learning. Curr. Biol. 16, 1422–1427.

Semedo, J.D., Zandvakili, A., Machens, C.K., Yu, B.M., and Kohn, A. (2019). Cortical Areas Interact through a Communication Subspace. Neuron.

Seriès, P., Latham, P.E., and Pouget, A. (2004). Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations. Nat Neurosci 7, 1129–1135.

210

A

Serino, A., Farnè, A., Rinaldesi, M.L., Haggard, P., and Làdavas, E. (2007). Can vision of the body ameliorate impaired somatosensory function? Neuropsychologia 45, 1101–1107.

Shams, L., and Beierholm, U.R. (2010). Causal inference in perception. Trends in Cognitive Sciences 14, 425–432.

Shams, L., and Seitz, A.R. (2008). Benefits of multisensory learning. Trends Cogn. Sci. 12, 411–417.

Shams, L., Kamitani, Y., and Shimojo, S. (2000). Illusions. What you see is what you hear. Nature 408, 788.

Shams, L., Wozny, D.R., Kim, R., and Seitz, A. (2011). Influences of multisensory experience on subsequent unisensory processing. Front Psychol 2, 264.

Sheppard, J.P., Raposo, D., and Churchland, A.K. (2013). Dynamic weighting of multisensory stimuli shapes decision-making in rats and humans. J Vis 13.

Sherman, S.M. (2005). Thalamic relays and cortical functioning. In Progress in Brain Research, (Elsevier), pp. 107–126.

Sherman, S.M., and Guillery, R.W. (1996). Functional organization of thalamocortical relays. J. Neurophysiol. 76, 1367–1395.

Shuler, M.G., and Bear, M.F. (2006). Reward timing in the primary visual cortex. Science 311, 1606–1609.

Siemann, J.K., Muller, C.L., Bamberger, G., Allison, J.D., Veenstra-VanderWeele, J., and Wallace, M.T. (2015). A novel behavioral paradigm to assess multisensory processing in mice. Front. Behav. Neurosci. 8, 456.

Sigala, N., Kusunoki, M., Nimmo-Smith, I., Gaffan, D., and Duncan, J. (2008). Hierarchical coding for sequential task events in the monkey prefrontal cortex. PNAS 105, 11969–11974.

Slutsky, D.A., and Recanzone, G.H. (2001). Temporal and spatial dependency of the

ventriloquism effect. NeuroReport 12, 7.

Smith, S., and Häusser, M. (2010). Parallel processing of visual space by neighboring neurons in mouse visual cortex. Nat Neurosci 13, 1144–1149.

Sompolinsky, H., Yoon, H., Kang, K., and Shamir, M. (2001). Population coding in neuronal systems with correlated noise. Phys. Rev. E 64, 051904.

Song, Y.-H., Kim, J.-H., Jeong, H.-W., Choi, I., Jeong, D., Kim, K., and Lee, S.-H. (2017). A Neural Circuit for Auditory Dominance over Visual Perception. Neuron 93, 940-954.e6.

Sparks, D.L. (2002). The brainstem control of saccadic eye movements. Nature Reviews Neuroscience 3, 952–964.

Spence, C. (2011). Crossmodal correspondences: a tutorial review. Atten Percept Psychophys 73, 971–995.

Stein, B.E., and Stanford, T.R. (2008). Multisensory integration: current issues from the perspective of the single neuron. Nat. Rev. Neurosci. 9, 255–266.

Stein, B.E., Stanford, T.R., and Rowland, B.A. (2009a). The neural basis of multisensory integration in the midbrain: its organization and maturation. Hear. Res. 258, 4–15.

Stein, B.E., Stanford, T.R., Ramachandran, R., Perrault, T.J., and Rowland, B.A. (2009b). Challenges in quantifying multisensory integration: alternative criteria, models, and inverse effectiveness. Exp Brain Res 198, 113–126.

Steinmetz, N.A., Koch, C., Harris, K.D., and Carandini, M. (2018). Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Current Opinion in Neurobiology 50, 92–100.

211

Appendices

Stevenson, R.A., Ghose, D., Fister, J.K., Sarko, D.K., Altieri, N.A., Nidiffer, A.R., Kurela, L.R., Siemann, J.K., James, T.W., and Wallace, M.T. (2014). Identifying and Quantifying Multisensory Integration: A Tutorial Review. Brain Topography.

Stokes, M.G., Kusunoki, M., Sigala, N., Nili, H., Gaffan, D., and Duncan, J. (2013). Dynamic Coding for Cognitive Control in Prefrontal Cortex. Neuron 78, 364–375.

Stosiek, C., Garaschuk, O., Holthoff, K., and Konnerth, A. (2003). In vivo two-photon calcium imaging of neuronal networks. PNAS 100, 7319–7324.

St-Pierre, F., Marshall, J.D., Yang, Y., Gong, Y., Schnitzer, M.J., and Lin, M.Z. (2014). High-fidelity optical reporting of neuronal electrical activity with an ultrafast fluorescent voltage sensor. Nat Neurosci 17, 884–889.

Stüttgen, M.C., Schwarz, C., and Jäkel, F. (2011). Mapping Spikes to Sensations. Front. Neurosci. 5.

Svoboda, K., Denk, W., Kleinfeld, D., and Tank, D.W. (1997). In vivo dendritic calcium dynamics in neocortical pyramidal neurons. Nature 385, 161–165.

Tafazoli, S., Safaai, H., De Franceschi, G., Rosselli, F.B., Vanzella, W., Riggi, M., Buffolo, F., Panzeri, S., and Zoccolan, D. (2017). Emergence of transformation-tolerant representations of visual objects in rat lateral extrastriate cortex. Elife 6.

Talsma, D., Senkowski, D., Soto-Faraco, S., and Woldorff, M.G. (2010). The multifaceted interplay between attention and multisensory integration. Trends Cogn. Sci. (Regul. Ed.) 14, 400–410.

Todd, J.W. (1912). Reaction to multiple stimuli (The Science Press).

Tononi, G., Boly, M., Massimini, M., and Koch, C. (2016). Integrated information theory: from consciousness to its physical substrate. Nat. Rev. Neurosci. 17, 450–461.

Tsodyks, M.V., and Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. PNAS 94, 719–723.

Ursino, M., Cuppini, C., and Magosso, E. (2014). Neurocomputational approaches to modelling multisensory integration in the brain: a review. Neural Netw 60, 141–165.

Vasconcelos, N., Pantoja, J., Belchior, H., Caixeta, F.V., Faber, J., Freire, M.A.M., Cota, V.R., Macedo, E.A. de, Laplagne, D.A., Gomes, H.M., et al. (2011). Cross-modal responses in the primary visual cortex encode complex objects and correlate with tactile discrimination. PNAS 108, 15408–15413.

Vergara, J., Rivera, N., Rossi-Pool, R., and Romo, R. (2016). A Neural Parametric Code for Storing Information of More than One Sensory Modality in Working Memory. Neuron 89, 54–62.

Vincis, R., and Fontanini, A. (2016). Associative learning changes cross-modal representations in the gustatory cortex. Elife 5.

Vinck, M., Batista-Brito, R., Knoblich, U., and Cardin, J.A. (2015). Arousal and Locomotion Make Distinct Contributions to Cortical Activity Patterns and Visual Encoding. Neuron 86, 740–754.

Vinje, W.E., and Gallant, J.L. (2000). Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision. Science 287, 1273–1276.

Von Békésy, G., and Wever, E.G. (1960). Experiments in hearing (McGraw-Hill New York).

Wallace, M.T., Meredith, M.A., and Stein, B.E. (1993). Converging influences from visual, auditory, and somatosensory cortices onto output neurons of the superior colliculus. J. Neurophysiol. 69, 1797–1809.

212

A

Wallace, M.T., Meredith, M.A., and Stein, B.E. (1998). Multisensory Integration in the Superior Colliculus of the Alert Cat. Journal of Neurophysiology 80, 1006–1010.

Wallace, M.T., Ramachandran, R., and Stein, B.E. (2004). A revised view of sensory cortical parcellation. PNAS 101, 2167–2172.

Wang, Q., and Burkhalter, A. (2007). Area map of mouse visual cortex. J Comp Neurol 502, 339–357.

Wang, Q., and Burkhalter, A. (2013). Stream-Related Preferences of Inputs to the Superior Colliculus from Areas of Dorsal and Ventral Streams of Mouse Visual Cortex. J. Neurosci. 33, 1696–1705.

Wang, Q., Gao, E., and Burkhalter, A. (2011). Gateways of Ventral and Dorsal Streams in Mouse Visual Cortex. J. Neurosci. 31, 1905–1918.

Wang, Q., Sporns, O., and Burkhalter, A. (2012). Network Analysis of Corticocortical Connections Reveals Ventral and Dorsal Processing Streams in Mouse Visual Cortex. J. Neurosci. 32, 4386–4399.

Wichmann, F.A., and Hill, N.J. (2001). The psychometric function: I. Fitting, sampling, and goodness of fit. Attention, Perception, & Psychophysics 63, 1293–1313.

Wiechert, M.T., Judkewitz, B., Riecke, H., and Friedrich, R.W. (2010). Mechanisms of pattern decorrelation by recurrent neuronal circuits. Nat Neurosci 13, 1003–1010.

Wimmer, R.D., Schmitt, L.I., Davidson, T.J., Nakajima, M., Deisseroth, K., and Halassa, M.M. (2015). Thalamic control of sensory selection in divided attention. Nature 526, 705–709.

Winters, B.D., and Reid, J.M. (2010). A Distributed Cortical Representation Underlies Crossmodal Object Recognition in Rats. J. Neurosci. 30, 6253–6261.

Yuste, R. (2015). From the neuron doctrine to neural networks. Nat Rev Neurosci 16, 487–497.

Zatka-Haas, P., Steinmetz, N.A., Carandini, M., and Harris, K.D. (2019). Distinct contributions of mouse cortical areas to visual discrimination. BioRxiv 501627.

Zhang, S., Xu, M., Kamigaki, T., Hoang Do, J.P., Chang, W.-C., Jenvay, S., Miyamichi, K., Luo, L., and Dan, Y. (2014). Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–665.

Zheng, Q.Y., Johnson, K.R., and Erway, L.C. (1999). Assessment of hearing in 80 inbred strains of mice by ABR threshold analyses. Hearing Research 130, 94–107.

Zoccolan, D., Graham, B.J., and Cox, D.D. (2010). A self-calibrating, camera-based eye tracker for the recording of rodent eye movements. Front. Neurosci. 4, 193.

Zohary, E., Shadlen, M.N., and Newsome, W.T. (1994). Correlated neuronal discharge rate and its implications for psychophysical performance. Nature 370, 140–143.

Zylberberg, J., Cafaro, J., Turner, M.H., Shea-Brown, E., and Rieke, F. (2016). Direction-Selective Circuits Shape Noise to Ensure a Precise Population Code. Neuron 89, 369–383.

213

Appendices

LIST OF PUBLICATIONSMeijer, G.T., Marchesi, P., Mejias, J.F.., Montijn, J.S., Lansink, C.S., Pennartz, C.M.A. Higher-order

visual cortex shows stronger neural correlates of visual and multisensory detection behavior compared to primary visual cortex. Manuscript submitted for publication.

Meijer, G.T., Mertens, P.E.C., Pennartz, C.M.A., Olcese, U.*, and Lansink, C.S.* (2019) Cortical networks for multisensory processing: distinct operations sharing a common circuitry. Progress in Neurobiology. DOI: 10.1016/j.pneurobio.2019.01.004. *Equal contribution

Bielefeld, P., Schouten, M., Meijer, G.T., Breuk, M.J., Geijtenbeek, K.W., Karayel, S., Tiaglik, A., Vuuregge, A., Willems, R.A.L., Witkamp, D., et al. (2019). Co-administration of Anti microRNA-124 and -137 Oligonucleotides Prevents Hippocampal Neural Stem Cell Loss Upon Non-convulsive Seizures. Frontiers in Molecular Neuroscience. 12, 31.

Meijer, G.T., Pie, J.L., Dolman, T.L., Pennartz, C.M.A., and Lansink, C.S. (2018). Audiovisual Integration Enhances Stimulus Detection Performance in Mice. Frontiers of Behavioral Neuroscience 12, 231.

Goltstein, P.M., Meijer, G.T., and Pennartz, C.M.A. (2018). Conditioning sharpens the spatial representation of rewarded stimuli in mouse primary visual cortex. eLife, DOI: 10.7554/eLife.37683.

Meijer, G.T., Montijn, J.S., Pennartz, C.M.A., and Lansink, C.S. (2017). Audiovisual modulation in mouse primary visual cortex depends on cross-modal stimulus configuration and congruency. Journal of Neuroscience. 37, 8783–8796.

Montijn, J.S.*, Meijer, G.T.*, Lansink, C.S., and Pennartz, C.M.A. (2016). Population-level neural codes are robust to single-neuron variability from a multidimensional coding perspective. Cell Reports 16, 2486–2498. *Equal contribution

Lansink, C.S., Meijer, G.T., Lankelma, J.V., Vinck, M.A., Jackson, J.C., and Pennartz, C.M.A. (2016). Reward expectancy strengthens CA1 theta and beta band synchronization and hippocampal-ventral striatal coupling. Journal of Neuroscience. 36, 10598–10610.

LIST OF POSTERSMeijer, G.T., Marchesi, P., Montijn, J.S., Pennartz, C.M.A. and Lansink, C.S. Multisensory integration

in the primary visual cortex and association area AL of the mouse during a stimulus detection task. Society for Neuroscience annual meeting, Washington, USA, November 2017.

Lansink, C.S., Meijer, G.T., Pie, J.L., Dolman, T.L. and Pennartz, C.M.A. Weighting of auditory and visual cues during cross-modal stimulus detection. Society for Neuroscience annual meeting, Washington, USA, November 2017.

Meijer, G.T., Montijn, J.S., Pennartz, C.M.A., and Lansink, C.S. Audiovisual modulation in mouse V1 depends on cross-modal stimulus configuration and congruency. European Visual Cortex meeting, London, UK, September 2017.

Dolman, T.L., Meijer, G.T., Montijn, J.S., Pennartz, C.M.A. and Lansink, C.S. Audio-visual integration increases perceptual performance in mice. EBPS meeting on Neural Assemblies & Population Coding, Amsterdam, September 2016.

Meijer, G.T., Montijn, J.S., Pennartz, C.M.A., and Lansink, C.S. Dynamic encoding of cross-modal stimuli in the primary visual cortex. Barrel Cortex Function meeting, Amsterdam, May 2016.

Meijer, G.T., Montijn, J.S., Lansink, C.S. and Pennartz, C.M.A. Chronic GCaMP6 imaging shows temporal stability of noise correlations: relevance for population coding. Society for Neuroscience annual meeting, Chicago, November 2015.

214

A

CONTRIBUTION OF CO-AUTHORS

Chapter 2 (Meijer et al., 2017)G.T.M., C.M.A.P., and C.S.L. designed research; G.T.M. performed research; J.S.M. contributed un-published reagents/analytic tools; G.T.M. and C.S.L. analyzed data; G.T.M., C.M.A.P., and C.S.L. wrote the paper.

Chapter 3 (Meijer et al., 2018)G.T.M., C.M.A.P., and C.S.L. designed the research, analyzed and interpreted the data, and wrote the manuscript. G.T.M. and T.L.D. performed the experimental work. J.L.P. contributed to analysis tools.

Chapter 4 (Meijer et al., submitted)G.T.M., C.M.A.P. and C.S.L. designed the research; G.T.M. performed the experimental work; G.T.M., C.S.L. and P.M. analyzed data; J.S.M. contributed analytic tools; J.F.M. built the computational model; G.T.M., C.M.A.P., and C.S.L. wrote the paper.

Chapter 5 (Montijn & Meijer et al., 2016)J.S.M. and G.T.M. performed the experiments and analyzed the data. G.T.M. developed the viral injec-tion protocols for GCaMP6 expression. J.S.M. and C.M.A.P. designed the experiments and analyses. J.S.M. wrote the paper. J.S.M., G.T.M., C.S.L., and C.M.A.P. discussed the results and contributed to the final manuscript.

Chapter 6: Part I (Meijer et al., 2019)G.T.M., P.E.C., C.M.A.P, U.O. and C.S.L. wrote the manuscript. G.T.M and P.E.C. created the figures.

215

Appendices

ABOUT THE AUTHORGuido Thomas Meijer (1988, Nieuwegein, The Netherlands) started his bachelor in Psychobiology at the University of Amsterdam in 2006. During this time he developed a passion for neuroscience, but was also an active member of the student association for Biology, Psychobiology and Biomedical Sciences ‘Congo’. He was member of the board in 2007-2008 and had a seat in a large number of committees. For his efforts he was declared an honorary member of the association by the general assembly in 2017.

After travelling through the continent of South America for half a year he started his masters program in Cognitive Neuroscience at the University of Amsterdam in 2010. During the masters program he did a nine month internship with Pieter Goltstein at the Cognitive and Systems Neuroscience lab researching how reward signals influence processing in the primary visual cortex using two-photon calcium imaging. This internship was followed by a six month internship at Harvard Medical School in Boston under the supervision of Mark Andermann researching how feelings of hunger are coded by neurons in the hypothalamus using extra-cellular electrophysiology and optogenetics.

After returning from the United States in 2013 he started his PhD at the Cognitive and Systems Neu-roscience lab at the University of Amsterdam under the supervision of Carien Lansink and Cyriel Pennartz. The result of which can be read in this thesis. During his PhD he was also the chairman of the alumni association for graduates of the Psychobiology bachelors programme. Other notable activities include a collaboration with the Gerrit Rietveld Academie for Fine Arts and Design and a publication in the popular science magazine ‘Amsterdam Science’.

In June 2018 he started as a scientist in the International Brain Laboratory (IBL) at the Champali-maud Centre for the Unknown in Lisbon in the lab of Zachary Mainen. The IBL is an international collaboration between 21 experimental and theoretical labs based in 5 different countries.

216

A

ACKNOWLEDGEMENTSA wise person once said to me ‘The only good thesis is a finished thesis’. It’s taken five years, a lot of sweat, a pinch of blood, but here I am, with a finished thesis. Whether it’s any good, I’ll leave for the defense committee to judge. And you of course, unless you’ve skipped directly to the acknowledge-ments without reading anything of the thesis. If that’s the case, welcome! I always do that too. I’ve made all the names bold so you can easily find yourself.

I’d like to thank my direct supervisor Carien Lansink. You’ve made me into the scientist that I am today. I’ve learned a lot from your sharp mind, moral fiber, goal-oriented focus, resilience, and posi-tive attitude. Admittedly, sometimes I got annoyed by the fact that everything that left your desk had to meet a very high standard. But I see now that this is exactly what makes you so incredibly good at your job and I thank you for it. I’d like to thank my promotor Cyriel Pennartz, your extensive knowledge on the topic and literature has lifted many discussions and papers to a higher level. In every scientific discussion you always seem to be able to quote at least three papers by heart including year and journal.

This thesis would have been of a much lower standard if it wasn’t for Jorrit’s help and teaching. When we first started working together on the project that was to become chapter 5, I admittedly had no clue what we were doing. Something about coding in high-dimensional space and correlations of correlations. But thanks to your knowledge of computational neuroscience I slowly started to un-derstand noise correlations, and their importance. Together with Quinten (misspelling is intentional) we battled our way through the ups and downs of life on Science Park. Staring at the bottom of our glasses when experiments failed, and raising them high when they succeeded. Quinten (a.k.a. the awkward rat), your randomness and wildly inappropriate jokes have lowered the standard of many discussions to the point that people actually stood up and left the lunch table. For that I salute you. One of these people was often Lianne. If the four of us were the Ninja Turtles, Lianne, you would be Donatello. You were the least prone to inappropriate randomness of the four of us, creating a necessary counterbalance. This seemingly serious and rational approach to life is strongly contrasted by your bursts of alcoholic debauchery which would make Dionysus proud. The Greek gods also smile down upon your efforts as my paranymph, an ancient Greek role which you are executing with excellence despite the stress that comes with finishing a PhD yourself. You were there for me when I was going through that stress myself and your support and ‘fuck it, let’s go drinking’ attitude pulled me through it, thank you.

My time at Science Park was further given color by Paul. Together we invented the ‘Groeten’, an open-source currency which finally provided a way ‘om iemand de groeten te doen’ in a tangible way: through the gift of a banknote of 5 or 10 Groeten. Or by means of some coins: de Groetjes. We also started a social media campaign to get Freestyler by Bomfunk MC to number 1 of the Top 2000, which as you might know if you listen to the Top 2000, failed miserably. Besides all these absurd

217

Appendices

projects we also found the time to discuss science and write a review together (Discussion part I). I also want to thank Charlotte; your unbounding liveliness and positivity could even make Tuesdays at Science Park fun. And I really hate Tuesdays. You were also an indispensable person at the vrijmibo in Polder where we had many hilarious nights which usually ended up with you not making it home and crashing on Lianne’s couch. Science Park was also stirred up by a Chilean delegation: Conrado and Ismael brought a bit of South America to my PhD and I’ll never forget our crazy conversations. In that category I also have to acknowledge Mathijs and Jean-Pie, who not only helped out with the sci-entific side (Jean-Pie contributed crucial analysis to Chapter 3) but were also a lot of fun on the social side of the lab. I’d also like to thank Pietro, the computational wizard, who could train five different decoders on my data before lunch and fit some multi-level statistical models in the afternoon, all with an admirable air of coolness. Thanks also to Tom for all your word jokes, Jeroen for your help with everything, Ivana for the slivovitz, Umberto for your amazing contribution to our review, Silviu for your inability to feel electrical shocks, Jeanette for our conversations on the visual system and Wim for everything you did for the education at Science Park.

My mentor in the art of two-photon imaging was definitely Pieter, you took me in when I was just a first-year master student and taught me how to do science. If it wasn’t for your guidance and mentor-ing, I wouldn’t have been offered this PhD position at the end of my internship and I would not be writing these acknowledgements now, thank you. We eventually even published an eLife paper on the finding that if you give enough vanilla pudding to mice, eventually their visual cortex changes. Truly groundbreaking stuff.

I went on to supervise students of my own, all of whom provided valuable contributions to various parts in this thesis: Julien, Thomas, Judith, Jesper, Stephan, Daphnee and Marleen all ended up on the ‘Wall of Reluctant Acceptance’. A place where I would hang a mugshot of all my students after they had finished an internship with me. They would hold a sign in front of them with their name and the period of their lives that they gave up to work for me without pay. I thank you for that. Julien, you were my first student and we had such weird interactions that nobody knew what to think of it. You would often allude to the things that would happen in the darkness of the two-photon room. Or you would act extremely subservient, just to freak people out. You also quite regularly stole my badge right out of my badge holder without me noticing. You liked the lab and started a PhD so suddenly we became colleagues which was incredible fun.

I also want to thank everybody else who made the SILS-CNS an amazing place to work: Lars, Wille-mieke, Rico, Dimi, Cindy, Pascal, Cato, Janske, Erik, Gerrald, Maralinde, Sylvie, Kit, Lianne, Gideon, Kitty, Elise, Tamar, Simone and Iris. Thanks for all the borrels that escalated and all the times we ended up in club Nasty. I couldn’t have done it without you.

218

A

Ik wissel naar het Nederlands want het is toch een Nederlandstalige podcast. Sicco, tijdens mijn PhD startte wij, samen met Sander, onze eigen podcast ‘Een Fris en Vurig Liedje’, de enige Neder-landstalige podcast over Game of Thrones. Jij hebt zoveel energie dat ik er zeker van ben dat je als kind in een ketel Red Bull bent gevallen. Je trok te kar binnen onze podcast en niet zonder resultaat, we wonnen de prijs voor de beste onafhankelijke Nederlandstalige podcast en hebben momenteel duizenden luisteraars! Bedankt voor je onuitputtelijke bron van energie en alle lol die we hadden met nerden over Game of Thrones.

Ik wil al mijn vrienden te bedanken die me gesteund hebben tijdens mijn PhD. Thomas, bedankt voor alle keren dat we voor of achter de DJ tafel stonden en voor alle hamburgers en alle halve theo’s. Jij was de dieselmotor, de machine, die me door alle dalen van de PhD heeft heengetrokken. Joyce, bedankt voor alle steun die ik van je heb gekregen tijdens mijn PhD en alle mooie tijden die we heb-ben gedeeld. Bedankt alle Bikkels van de Brouwerij waarmee ik elke donderdag opnieuw aanschoof in de Brouwerij ‘t IJ om de week door te spreken, te klagen en grappen te maken: Brenda, José, Bart A, Bart vdM, Dominique, Frank, Jurrien, Renee, Mark en Loes. Verder wil ik mijn band bedanken voor alle jam sessies en belachelijke gesprekken: Thomas, Peter en Quincy. De mannen van de bereboot voor alle tochten over de grachten, alle bere-mooie avonden in de Skinny Bridge en de Engelenbewaarder: Johan, Ruben, Angelo, Gertjan en Olaf. Bedankt ook de party-crew voor alle festivallen en feesten, ik heb alle glitter volgens mij nog steeds niet van mezelf af gekregen: Sam, Sophie, Lisa, Lenny en Kim. Verder wil ik nog mijn film-crew, Sander, Ids en Ravi bedanken. We hebben zo’n beetje elke actiefilm gekeken die in de bioscoop kwam en ik ben enorm verdrietig dat ik Avengers: Endgame zonder jullie zal zien. Ik hoop dat hij op de schaal Batman v Superman – Iron Man 1 een dikke Rogue One is. Bedankt ook Jordi en Esther voor onze prachtige tijd op het Kruger-plein, het was de beste plek die ik me kon wensen in het laatste jaar van mijn PhD.

Ik wil mijn ouders Rob en Elly en mijn broertje Arthur bedanken dat jullie me altijd gestimuleerd hebben om het beste uit mezelf te halen en te doen waar ik gelukkig van word. Jullie hebben me altijd gesteund tijdens mijn PhD, ook al begrepen jullie volgens mij niet echt wat ik deed, en waarom ik het zo belangrijk vond. Ik ben bang dat dit boek niet heel veel verheldering zal brengen. Daar gaat het ook helemaal niet om, het gaat erom dat jullie me steunen in alles wat ik doe en daar dank ik jullie voor.

Alex, ik weet niet of het zonder jou ooit af was gekomen. Je steunt me als ik er doorheen zit en je pusht me als ik me aanstel en een duwtje nodig heb. Je was een ongelofelijke steun tijdens de laatste loodjes, die zelfs onder de prachtige Portugese zon, erg zwaar waren. Je bent in het diepe gesprongen en naar Lissabon verhuist wat ik super dapper van je vind. We zitten nu samen in deze belachelijke rollercoaster van het leven en dat maakt mij de gelukkigste man ter wereld.