Cargando…

Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning

First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; glob...

Descripción completa

Detalles Bibliográficos
Autor principal:	Rolls, Edmund T.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2021
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8335547/ https://www.ncbi.nlm.nih.gov/pubmed/34366818 http://dx.doi.org/10.3389/fncom.2021.686239

Ejemplares similares

Learning view invariant recognition with partially occluded objects
por: Tromans, James M., et al.
Publicado: (2012)

An intrinsic value system for developing multiple invariant representations with incremental slowness learning
por: Luciw, Matthew, et al.
Publicado: (2013)

Exploiting temporal continuity of views to learn visual object invariance
por: Bart, Evgeniy, et al.
Publicado: (2013)

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet
por: Rolls, Edmund T.
Publicado: (2012)

Deformation-specific and deformation-invariant visual object recognition: pose vs. identity recognition of people and deforming objects
por: Webb, Tristan J., et al.
Publicado: (2014)

Learned Non-Rigid Object Motion is a View-Invariant Cue to Recognizing Novel Objects
por: Chuang, Lewis L., et al.
Publicado: (2012)

Unsupervised invariance learning of transformation sequences in a model of object recognition yields selectivity for non-accidental properties
por: Parker, Sarah M., et al.
Publicado: (2015)

Unsupervised Facial Action Representation Learning by Temporal Prediction
por: Wang, Chongwen, et al.
Publicado: (2022)

Event-Based, Timescale Invariant Unsupervised Online Deep Learning With STDP
por: Thiele, Johannes C., et al.
Publicado: (2018)

Neural Representations that Support Invariant Object Recognition
por: Goris, Robbe L. T., et al.
Publicado: (2009)

Spatial position constraint for unsupervised learning of speech representations
por: Humayun, Mohammad Ali, et al.
Publicado: (2021)

Embodied Object Representation Learning and Recognition
por: Van de Maele, Toon, et al.
Publicado: (2022)

Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception
por: Brucklacher, Matthias, et al.
Publicado: (2023)

Invariant visual object recognition: biologically plausible approaches
por: Robinson, Leigh, et al.
Publicado: (2015)

Invariant representations of mass in the human brain
por: Schwettmann, Sarah, et al.
Publicado: (2019)

Chemosensory Learning in the Cortex
por: Rolls, Edmund T.
Publicado: (2011)

Chemosensory learning and memory
por: Gallo, Milagros, et al.
Publicado: (2012)

Invariant representation of physical stability in the human brain
por: Pramod, RT, et al.
Publicado: (2022)

Unsupervised Representation Learning for Proteochemometric Modeling
por: Kim, Paul T., et al.
Publicado: (2021)

Unsupervised Temporal Contiguity Experience Does Not Break the Invariance of Orientation Selectivity Across Spatial Frequency
por: Crijns, Els, et al.
Publicado: (2019)

Unsupervised learning is crucial to learning the names of objects
por: Lillicrap, Timothy P, et al.
Publicado: (2007)

Discrimination of Movement-Related Cortical Potentials Exploiting Unsupervised Learned Representations From ECoGs
por: Loza, Carlos A., et al.
Publicado: (2019)

Unimodal statistical learning produces multimodal object-like representations
por: Lengyel, Gábor, et al.
Publicado: (2019)

Unsupervised Learning of Persistent and Sequential Activity
por: Pereira, Ulises, et al.
Publicado: (2020)

Unsupervised learning of haptic material properties
por: Metzger, Anna, et al.
Publicado: (2022)

The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks
por: Halvagal, Manu Srinath, et al.
Publicado: (2023)

Learning Invariant Representations using Mutual Information Regularization
por: Tan, Justin
Publicado: (2019)

Spatial Representations in the Human Brain
por: Herweg, Nora A., et al.
Publicado: (2018)

Learning unsupervised contextual representations for medical synonym discovery
por: Schumacher, Elliot, et al.
Publicado: (2019)

Hebbian Crosstalk Prevents Nonlinear Unsupervised Learning
por: Cox, Kingsley J. A., et al.
Publicado: (2009)

Unsupervised learning of facial emotion decoding skills
por: Huelle, Jan O., et al.
Publicado: (2014)

Systems view on spatial planning and perception based on invariants in agent-environment dynamics
por: Mettler, Bérénice, et al.
Publicado: (2015)

Learning Domain-Invariant Representations of Histological Images
por: Lafarge, Maxime W., et al.
Publicado: (2019)

Finding and recognizing objects in natural scenes: complementary computations in the dorsal and ventral visual systems
por: Rolls, Edmund T., et al.
Publicado: (2014)

Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks
por: Weidel, Philipp, et al.
Publicado: (2021)

Unsupervised learning of digit recognition using spike-timing-dependent plasticity
por: Diehl, Peter U., et al.
Publicado: (2015)

Tracking prototype and exemplar representations in the brain across learning
por: Bowman, Caitlin R, et al.
Publicado: (2020)

Supervised and Unsupervised Learning Technology in the Study of Rodent Behavior
por: Gris, Katsiaryna V., et al.
Publicado: (2017)

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature
por: Frisoni, Giacomo, et al.
Publicado: (2021)

Unsupervised generative and graph representation learning for modelling cell differentiation
por: Bica, Ioana, et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_91p8828103kao78knnmm0aep9p