How Are Complex Cell Properties Adapted to the Statistics of Natural Stimuli?

Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex

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Receptive field organization of complex cells in the cat's striate cortex.

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Representation Learning: A Review and New Perspectives

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Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives

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The statistics of natural hand movements

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Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis

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Slow feature analysis yields a rich repertoire of complex cell properties

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The visual ecology of fiddler crabs

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A model of the ventral visual system based on temporal stability and local memory

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Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling

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Processing of complex stimuli and natural scenes in the visual cortex

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How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target PropagationPreprint

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Salient features in gaze-aligned recordings of human visual input during free exploration of natural environments

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Laminar processing in the visual cortical column.

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Toward an integration of deep learning and neuroscience

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Human eye-head co-ordination in natural exploration

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Statistics of Natural Movements Are Reflected in Motor Errors

Howard, I. S., Ingram, J. N., Kording, K. P., & Wolpert, D. M. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00013.2009 · Cited by 108

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

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A Maximum-Likelihood Interpretation for Slow Feature Analysis

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Local field potentials indicate network state and account for neuronal response variability

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Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines

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Predictive coding is a consequence of energy efficiency in recurrent neural networks

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Complex cell pooling and the statistics of natural images

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Slowness: An Objective for Spike-Timing–Dependent Plasticity?

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Deep Neural Networks in Computational NeurosciencePreprint

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Deep Learning of Representations

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Topography of vision and behaviour

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Slow, Decorrelated Features for Pretraining Complex Cell-like Networks

Bengio, Y., & Bergstra, J. (2009) · neural information processing systems · Cited by 61

Naturalistic approaches to sensorimotor control

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Orientation Tuning, But Not Direction Selectivity, Is Invariant to Temporal Frequency in Primary Visual Cortex

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Learning viewpoint invariant object representations using a temporal coherence principle

Einhäuser, W., Hipp, J. F., Eggert, J. P., Körner, E., & Peter König, B. (2005) · Biological Cybernetics · doi:10.1007/s00422-005-0585-8 · Cited by 47

Symbols as Self-emergent Entities in an Optimization Process of Feature Extraction and Predictions

Peter König, B., & Krüger, N. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0050-3 · Cited by 45

Reverse-Engineering Deep ReLU NetworksPreprint

Rolnick, D., & Kording, K. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.00744 · Cited by 44

Temporal stability of stimulus representation increases along rodent visual cortical hierarchies

Piasini, E., Soltuzu, L., Muratore, P., Caramellino, R., Vinken, K., de Beeck, H. O., Balasubramanian, V., & Zoccolan, D. (2021) · Nature Communications · doi:10.1038/s41467-021-24456-3 · Cited by 44

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

Berkes, P., Turner, R. E., & Sahani, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000495 · Cited by 42

On the Analysis and Interpretation of Inhomogeneous Quadratic Forms as Receptive Fields

Berkes, P., & Wiskott, L. (2006) · Neural Computation · doi:10.1162/neco.2006.18.8.1868 · Cited by 40

Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model

Masquelier, T. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0361-9 · Cited by 41

Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics

Schwartz, O., B, T. J. S. A., & Dayan, P. (2006) · Neural Computation · doi:10.1162/neco.2006.18.11.2680 · Cited by 41

Neuronal variability and tuning are balanced to optimize naturalistic self-motion coding in primate vestibular pathways

Mitchell, D. E., Kwan, A., Carriot, J., Chacron, M. J., & Cullen, K. E. (2018) · eLife · doi:10.7554/elife.43019 · Cited by 33

Nonlinear and higher-order approaches to the encoding of natural scenes

Zetzsche, C., & Nuding, U. (2005) · Network Computation in Neural Systems · doi:10.1080/09548980500463982 · Cited by 33

Normative theory of visual receptive fields

Lindeberg, T. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e05897 · Cited by 32

Learning Complex Cell Invariance from Natural Videos: A Plausibility Proof

Masquelier, T., Serre, T., Thorpe, S. J., & Poggio, T. (2007) · Defense Technical Information Center (DTIC) · doi:10.21236/ada477541 · Cited by 32

Unsupervised experience with temporal continuity of the visual environment is causally involved in the development of V1 complex cells

Matteucci, G., & Zoccolan, D. (2020) · Science Advances · doi:10.1126/sciadv.aba3742 · Cited by 31

Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network

Ukita, J., Yoshida, T., & Ohki, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-40535-4 · Cited by 30

Nonlinear dimensionality reduction using a temporal coherence principle

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Distributed Adaptive Control: A Proposal on the Neuronal Organization of Adaptive Goal Oriented Behavior

Duff, A., Rennó‐Costa, C., Marcos, E., Luvizotto, A., Giovannucci, A., Sánchez-Fibla, M., Bernardet, U., & Verschure, P. F. M. J. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05181-4_2 · Cited by 26

Contrast-dependent surround suppression models for contour detection

Tang, Q., Sang, N., & Liu, H. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.05.009 · Cited by 23

NMF, LNMF, and DNMF modeling of neural receptive fields involved in human facial expression perception

Buciu, I., & Pitas, I. (2006) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2006.06.001 · Cited by 23

Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli

Ravello, C., Perrinet, L., Escobar, M.-J., & Palacios, A. G. (2019) · Scientific Reports · doi:10.1038/s41598-018-36861-8 · Cited by 22

Provably Scale-Covariant Continuous Hierarchical Networks Based on Scale-Normalized Differential Expressions Coupled in Cascade

Lindeberg, T. (2019) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-019-00915-x · Cited by 23

Hierarchical temporal prediction captures motion processing along the visual pathway

Singer, Y., Taylor, L., Willmore, B. D. B., King, A. J., & Harper, N. S. (2023) · eLife · doi:10.7554/elife.52599 · Cited by 25

Incremental Slow Feature Analysis

Kompella, V. R., Luciw, M., & Schmidhuber, J. (2011) · Cited by 19

Unsupervised learning of mid-level visual representations

Matteucci, G., Piasini, E., & Zoccolan, D. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102834 · Cited by 19

An edge-simplicity bias in the visual input to young infants

Anderson, E. M., Candy, T. R., Gold, J. M., & Smith, L. B. (2024) · Science Advances · doi:10.1126/sciadv.adj8571 · Cited by 21

Slowness and Sparseness Have Diverging Effects on Complex Cell Learning

Lies, J.-P., Häfner, R., & Bethge, M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003468 · Cited by 18

Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields

Haslinger, R., Pipa, G., Lima, B., Singer, W., Brown, E. N., & Neuenschwander, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039699 · Cited by 20

Spike timing-based unsupervised learning of orientation, disparity, and motion representations in a spiking neural network

Barbier, T., Teulière, C., & Triesch, J. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00152 · Cited by 17

Unifying perceptual and behavioral learning with a correlative subspace learning rule

Duff, A., & Verschure, P. F. M. J. (2010) · Neurocomputing · doi:10.1016/j.neucom.2009.11.048 · Cited by 17

Functional Organization of Temporal Frequency Selectivity in Primate Visual Cortex

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Normative theory of visual receptive fields

Lindeberg, T. (2017) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 17

Slow Feature Discriminant Analysis and its application on handwritten digit recognition

Huang, Y., Zhao, J., Tian, M., Zou, Q., & Luo, S. (2009) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2009.5178596 · Cited by 16

Sensory Adaptation and Short Term Plasticity as Bayesian Correction for a Changing Brain

Stevenson, I. H., Cronin, B., Sur, M., & Kording, K. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012436 · Cited by 16

Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network

Finger, H., & Peter König, B. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00195 · Cited by 16

Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex

Jia, X., Hong, H., & DiCarlo, J. J. (2021) · eLife · doi:10.7554/elife.60830 · Cited by 18

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

Dähne, S., Wilbert, N., & Wiskott, L. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003564 · Cited by 17

Predictions in the light of your own action repertoire as a general computational principle

Peter König, B., Wilming, N., Kaspar, K., Nagel, S. K., & Onat, S. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12002294 · Cited by 13

Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds

Carlin, M. A., & Elhilali, M. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002982 · Cited by 12

A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells

Sprekeler, H., & Wiskott, L. (2010) · Neural Computation · doi:10.1162/neco_a_00072 · Cited by 11

A Unifying Approach to High- and Low-Level Cognition

Peter König, B., Kühnberger, K., & Kietzmann, T. C. (2013) · De Gruyter eBooks · doi:10.1515/9783110313680.117 · Cited by 14

Efficient processing of natural scenes in visual cortex

Teşileanu, T., Piasini, E., & Balasubramanian, V. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.1006703 · Cited by 11

Outdoor Self-Localization of a Mobile Robot Using Slow Feature Analysis

Metka, B., Franzius, M., & Bauer-Wersing, U. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42054-2_32 · Cited by 10

A dual fast and slow feature interaction in biologically inspired visual recognition of human action

Yousefi, B., & Loo, C. K. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.10.021 · Cited by 10

Involving Motor Capabilities in the Formation of Sensory Space Representations

Weiller, D., Märtin, R., Dähne, S., Engel, A. K., & Peter König, B. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010377 · Cited by 10

Bio-inspired visual self-localization in real world scenarios using Slow Feature Analysis

Metka, B., Franzius, M., & Bauer-Wersing, U. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203994 · Cited by 11

Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex

Teichmann, M., Wiltschut, J., & Hamker, F. H. (2012) · Neural Computation · doi:10.1162/neco_a_00268 · Cited by 10

Perceptual Principles for Video Classification With Slow Feature Analysis

Thériault, C., Thome, N., Cord, M., & Pérez, P. (2014) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2014.2315742 · Cited by 8

Analysis and interpretation of quadratic models of receptive fields

Berkes, P., & Wiskott, L. (2007) · Nature Protocols · doi:10.1038/nprot.2007.27 · Cited by 8

The Portilla-Simoncelli Texture Model: towards Understanding the Early Visual Cortex

Vacher, J., & Briand, T. (2021) · Image Processing On Line · doi:10.5201/ipol.2021.324 · Cited by 10

Learning of somatosensory representations for texture discrimination using a temporal coherence principle

Hipp, J. F., Einhäuser, W., Conradt, J., & Peter König, B. (2005) · Network Computation in Neural Systems · doi:10.1080/09548980500361582 · Cited by 8

An inductive bias for slowly changing features in human reinforcement learning

Hedrich, N. L., Schulz, E., Hall-McMaster, S., & Schuck, N. W. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012568 · Cited by 7

Unsupervised learning of reflexive and action-based affordances to model adaptive navigational behavior

Weiller, D. (2010) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2010.00002 · Cited by 7

A Hierarchical Generative Model for Overcomplete Topographic Representations in Natural Images

Ma, L., & Zhang, L. (2007) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2007.4371128 · Cited by 7

Quantifying Utricular Stimulation During Natural Behavior

Rivera, A. R. V., Davis, J. L., Grant, W., Blob, R. W., PETERSON, E. H., NEIMAN, A. B., & ROWE, M. (2012) · Journal of Experimental Zoology Part A Ecological Genetics and Physiology · doi:10.1002/jez.1739 · Cited by 7

The 'tree-dependent components' of natural scenes are edge filters

Zoran, D., & Weiss, Y. (2009) · Cited by 7

Identifying Weights and Architectures of Unknown ReLU NetworksPreprint

Rolnick, D., & Kording, K. P. (2019) · arXiv (Cornell University) · Cited by 7

Data augmentation and image understandingPreprint

Hernández-García, Á. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14185 · Cited by 7

Sampling Visual Space: Topography, colour vision and visually guided predator avoidance in fiddler crabs (Uca vomeris)

Smolka, J. (2009) · ANU Open Research (Australian National University) · doi:10.25911/5d7a28557a843 · Cited by 6

Orientation selectivity properties for the affine Gaussian derivative and the affine Gabor models for visual receptive fields

Lindeberg, T. (2025) · Journal of Computational Neuroscience · doi:10.1007/s10827-024-00888-w · Cited by 5

EVOLVE·INFOMAX: An Unsupervised Learning Principle of Invariances for Nonlinear Dynamic Systems

Gao, X., & Shardt, Y. A. W. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.2c03330 · Cited by 5

Temporal slowness as an unsupervised learning principle: self-organization of complex-cell receptive fields and application to pattern recognition

Mlynek, J., Buckhout, T. J., Wiskott, L., Peter König, B., & Herz, A. V. M. (2005) · Cited by 5

Improving Robustness of Slow Feature Analysis Based Localization Using Loop Closure Events

Metka, B., Franzius, M., & Bauer-Wersing, U. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44781-0_58 · Cited by 5

Visual motion computation in recurrent neural networksPreprint

Pachitariu, M., & Sahani, M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/099101 · Cited by 5

Using Natural Scenes to Enhance our Understanding of the Cerebral Cortex's Role in Visual Search

Segraves, M. A. (2023) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100720-124033 · Cited by 4

Do the receptive fields in the primary visual cortex span a variability over the degree of elongation of the receptive fields?

Lindeberg, T. (2025) · Journal of Computational Neuroscience · doi:10.1007/s10827-025-00907-4 · Cited by 4

Quantifying the role of neurons for behavior is a mediation question

Jones, I. S., & Kording, K. P. (2019) · Behavioral and Brain Sciences · doi:10.1017/s0140525x19001444 · Cited by 4

How Does Artificial Intelligence Contribute to iEEG Research?

Berezutskaya, J., Saive, A.-L., Jerbi, K., & van Gerven, M. A. J. (2023) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-031-20910-9_47 · Cited by 3

Development of Fast Incremental Slow Feature Analysis (F-IncSFA)

Yousefi, B., & Loo, C. K. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252847 · Cited by 3

Learning the selectivity of V2 and V4 neurons using non-linear multi-layer wavelet networks

Nuding, U., & Zetzsche, C. (2006) · Biosystems · doi:10.1016/j.biosystems.2006.04.025 · Cited by 3

Learning Temporally Stable Representations from Natural Sounds: Temporal Stability as a General Objective Underlying Sensory Processing

Duff, A., Wyss, R., & Verschure, P. F. M. J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74695-9_14 · Cited by 3

Color and luminance processing in V1 complex cells and artificial neural networks

Bun, L. M., & Horwitz, G. D. (2023) · Color Research & Application · doi:10.1002/col.22903 · Cited by 2

Data-driven modelling of visual receptive fields: comparison between the generalized quadratic model and the nonlinear input model

Almasi, A., Sun, S. H., Jung, Y. J., Ibbotson, M. R., & Hamish Meffin, A. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad5d15 · Cited by 2

Learning the invariance properties of complex cells from their responses to natural stimuli

Einhäuser, W., Kayser, C., König, P., & Körding, K. P. (2002) · European Journal of Neuroscience · doi:10.1046/j.0953-816x.2001.01885.x · Cited by 91

Modeling the responses of V1 complex cells to natural temporal inputs

Ersoy, B., Kagan, I., Rucci, M., & Snodderly, D. M. (2004) · Journal of Vision · doi:10.1167/4.8.278 · Cited by 2

Natural Scene Evoked Population Dynamics across Cat Primary Visual Cortex Captured with Voltage-Sensitive Dye Imaging

Onat, S., Peter König, B., & Jancke, D. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr038 · Cited by 24

Response properties of neurons in the visual cortex of the rat

Burne, R. A., Parnavelas, J. G., & Lin, C.-S. (1984) · Experimental Brain Research · doi:10.1007/bf00238168 · Cited by 56

Causal adaptation to visual input dynamics governs the development of complex cells in V1Preprint

Matteucci, G., & Zoccolan, D. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/756668