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Hierarchical temporal prediction captures motion processing from retina to higher visual cortex

https://doi.org/10.1101/575464
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Visual development in primates: Neural mechanisms and critical periods
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Predictive coding
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Extracting Slow Subspaces from Natural Videos Leads to Complex Cells
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Pattern Recognition Mechanisms
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The Analysis of Moving Visual Patterns
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Optimal Prediction in the Retina and Natural Motion Statistics
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Classifying simple and complex cells on the basis of response modulation
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Cat and monkey retinal ganglion cells and their visual functional roles
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Spatiotemporal Elements of Macaque V1 Receptive Fields
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Theories of Error Back-Propagation in the Brain
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A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images
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The “independent components” of natural scenes are edge filters
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Sparse coding with an overcomplete basis set: A strategy employed by V1?
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A model of neuronal responses in visual area MT
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End-Stopping and the Aperture Problem
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
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Emergence of complex cell properties by learning to generalize in natural scenes
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Using goal-driven deep learning models to understand sensory cortex
inferredcites →https://doi.org/10.1038/nn.4385openalex-snapshot
Experience-dependent spatial expectations in mouse visual cortex
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Dynamics of motion signaling by neurons in macaque area MT
inferredcites →https://doi.org/10.1038/nn1786openalex-snapshot
How MT cells analyze the motion of visual patterns
inferredcites →https://doi.org/10.1073/pnas.1711114115openalex-snapshot
Toward a unified theory of efficient, predictive, and sparse coding
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A simple white noise analysis of neuronal light responses
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Predictive coding: a fresh view of inhibition in the retina
inferredcites →https://doi.org/10.1098/rspb.1998.0303openalex-snapshot
Independent component filters of natural images compared with simple cells in primary visual cortex
inferredcites →https://doi.org/10.1098/rspb.1998.0577openalex-snapshot
Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex
inferredcites →https://doi.org/10.1101/458737openalex-snapshot
The emergence of multiple retinal cell types through efficient coding of natural movies
inferredcites →https://doi.org/10.1101/511535openalex-snapshot
A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs
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Learning sparse, overcomplete representations of time-varying natural images
inferredcites →https://doi.org/10.1109/tit.1955.1055126openalex-snapshot
Predictive coding--I
inferredcites →https://doi.org/10.1111/ejn.13223openalex-snapshot
Direction selectivity of neurons in the visual cortex is non‐linear and lamina‐dependent
inferredcites →https://doi.org/10.1113/jphysiol.1953.sp004829openalex-snapshot
Summation and inhibition in the frog's retina
inferredcites →https://doi.org/10.1113/jphysiol.1959.sp006308openalex-snapshot
Receptive fields of single neurones in the cat's striate cortex
inferredcites →https://doi.org/10.1113/jphysiol.1968.sp008455openalex-snapshot
Receptive fields and functional architecture of monkey striate cortex
inferredcites →https://doi.org/10.1113/jphysiol.1974.sp010452openalex-snapshot
Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey
inferredcites →https://doi.org/10.1113/jphysiol.1978.sp012488openalex-snapshot
Spatial summation in the receptive fields of simple cells in the cat's striate cortex.
inferredcites →https://doi.org/10.1113/jphysiol.1978.sp012489openalex-snapshot
Receptive field organization of complex cells in the cat's striate cortex.
inferredcites →https://doi.org/10.1146/annurev.neuro.31.060407.125533openalex-snapshot
Mechanisms Underlying Development of Visual Maps and Receptive Fields
inferredcites →https://doi.org/10.1152/jn.00149.2003openalex-snapshot
How Are Complex Cell Properties Adapted to the Statistics of Natural Stimuli?
inferredcites →https://doi.org/10.1152/jn.1953.16.1.37openalex-snapshot
DISCHARGE PATTERNS AND FUNCTIONAL ORGANIZATION OF MAMMALIAN RETINA
inferredcites →https://doi.org/10.1152/jn.1965.28.2.229openalex-snapshot
RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT
inferredcites →https://doi.org/10.1162/089976600300015312openalex-snapshot
Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces
inferredcites →https://doi.org/10.1162/089976601753195969openalex-snapshot
Predictability, Complexity, and Learning
inferredcites →https://doi.org/10.1162/089976602317318938openalex-snapshot
Slow Feature Analysis: Unsupervised Learning of Invariances
inferredcites →https://doi.org/10.1162/089976603321192121openalex-snapshot
Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video
inferredcites →https://doi.org/10.1162/08997660360675026openalex-snapshot
Learning the Nonlinearity of Neurons from Natural Visual Stimuli
inferredcites →https://doi.org/10.1162/089976606775093936openalex-snapshot
Topographic Product Models Applied to Natural Scene Statistics
inferredcites →https://doi.org/10.1162/neco.1991.3.2.194openalex-snapshot
Learning Invariance from Transformation Sequences
inferredcites →https://doi.org/10.1162/neco.2008.01-07-455openalex-snapshot
Predictive Coding and the Slowness Principle: An Information-Theoretic Approach
inferredcites →https://doi.org/10.1162/neco_a_00247openalex-snapshot
Learning Intermediate-Level Representations of Form and Motion from Natural Movies
inferredcites →https://doi.org/10.1167/2.10.130openalex-snapshot
Neural correlates of shape-from-shading
inferredcites →https://doi.org/10.1167/2.7.130openalex-snapshot
Sparse coding of time-varying natural images
inferredcites →https://doi.org/10.1167/5.10.9openalex-snapshot
An oblique effect for local motion: Psychophysics and natural movie statistics
inferredcites →https://doi.org/10.1167/5.6.9openalex-snapshot
Slow feature analysis yields a rich repertoire of complex cell properties
inferredcites →https://doi.org/10.1167/6.11.13openalex-snapshot
Spatial four-alternative forced-choice method is the preferred psychophysical method for naive observers
inferredcites →https://doi.org/10.1364/josaa.2.000284openalex-snapshot
Spatiotemporal energy models for the perception of motion
inferredcites →https://doi.org/10.1364/josaa.20.001237openalex-snapshot
Bubbles: a unifying framework for low-level statistical properties of natural image sequences
inferredcites →https://doi.org/10.1371/journal.pcbi.1000495openalex-snapshot
A Structured Model of Video Reproduces Primary Visual Cortical Organisation
inferredcites →https://doi.org/10.1371/journal.pcbi.1003468openalex-snapshot
Slowness and Sparseness Have Diverging Effects on Complex Cell Learning
inferredcites →https://doi.org/10.1371/journal.pcbi.1003716openalex-snapshot
On the Use of Human Mobility Proxies for Modeling Epidemics
inferredcites →https://doi.org/10.1371/journal.pcbi.1003844openalex-snapshot
Multi-state Modeling of Biomolecules
inferredcites →https://doi.org/10.1371/journal.pcbi.1005058openalex-snapshot
The Representation of Prediction Error in Auditory Cortex
inferredcites →https://doi.org/10.1523/jneurosci.0404-13.2013openalex-snapshot
Emergence of Orientation Selectivity in the Mammalian Visual Pathway
inferredcites →https://doi.org/10.1523/jneurosci.16-23-07733.1996openalex-snapshot
Visual Response Properties of Striate Cortical Neurons Projecting to Area MT in Macaque Monkeys
inferredcites →https://doi.org/10.1523/jneurosci.22-13-05639.2002openalex-snapshot
Orientation Selectivity in Macaque V1: Diversity and Laminar Dependence
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.5860/choice.43-0626openalex-snapshot
The Cognitive neurosciences
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Possible Principles Underlying the Transformations of Sensory Messages
inferredcites →https://doi.org/10.7554/elife.31557openalex-snapshot
Sensory cortex is optimized for prediction of future input

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/b978-0-12-809324-5.24188-6openalex-snapshot
Feedback Systems: Descending Pathways and Adaptive Coding in the Auditory System
inferred← citeshttps://doi.org/10.1098/rsif.2025.0738openalex-snapshot
TiDHy: timescale demixing via hypernetworks to learn simultaneous dynamics from mixed observations
inferred← citeshttps://doi.org/10.1101/2021.02.22.432194openalex-snapshot
Dynamic Predictive Coding with Hypernetworks
inferred← citeshttps://doi.org/10.1101/2022.02.11.479668openalex-snapshot
Omission responses in field potentials but not spikes in rat auditory cortex
inferred← citeshttps://doi.org/10.1101/2023.10.02.560418openalex-snapshot
Cochlear tuning characteristics arise from temporal prediction of natural sounds
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1011801openalex-snapshot
Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex
inferred← citeshttps://doi.org/10.3389/fncom.2023.1207361openalex-snapshot
Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception
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