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Pooling in a predictive model of V1 explains functional and structural diversity across species
Neurons in the primary visual cortex are selective to orientation with various degrees of selectivity to the spatial phase, from high …
Cite DOI URL bioRxiv
Pooling in a predictive model of V1 explains functional and structural diversity across species
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to …
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Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Modelling Complex-cells and topological structure in the visual cortex of mammals using Sparse Predictive Coding
Cells in the primary visual cortex of mammals (V1) have historically been divided into two classes: simple and complex. Simple cells …
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Modelling Complex-cells and topological structure in the visual cortex of mammals using Sparse Predictive Coding
Cells in the primary visual cortex of mammals (V1) have historically been divided into two classes: simple and complex. Simple cells …
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Effect of top-down connections in Hierarchical Sparse Coding
Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images. The …
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Effect of top-down connections in Hierarchical Sparse Coding
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to …
Cite URL arXiv
A hierarchical, multi-layer convolutional sparse coding algorithm based on predictive coding
Sparse coding holds the idea that signals can be concisely described as a linear mixture of few components (called atoms) picked from a …
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Modelling Complex Cells of Early Visual Cortex using Predictive Coding
see a follow-up in: Victor Boutin, Angelo Franciosini, Frédéric Chavane, Franck Ruffier, Laurent U Perrinet (2021). Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system. PLoS Computational Biology.
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Sparse Deep Predictive Coding to model visual object recognition
If you’re at #sfn2019 and have an interest in #sparse #deep Predictive Coding, checkout @VictorBoutin ‘s poster 403.16 / P20:https://t.co/2VLEsl98oU It shows today + comes with a (timely) preprint https://t.co/FfKi9tjqrN !
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Sparse Deep Predictive Coding to model visual object recognition
Top-down connection in Hierarchical Sparse Coding
The brain has to solve inverse problems to correctly interpret sensory data and infer the set of causes that generated the sensory …
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From biological vision to unsupervised hierarchical sparse coding
The formation of connections between neural cells is essentially emerging from an unsupervised learning process. During the development …
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M2APix: a bio-inspired auto-adaptive visual sensor for robust ground height estimation
This paper presents for the first time the embedded stand-alone version of the bio-inspired M2APix (Michaelis-Menten auto-adaptive …
Cite URL HAL
On the Origins of Hierarchy in Visual Processing
It is widely assumed that visual processing follows a forward sequence of processing steps along a hierarchy of laminar sub-populations …
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Unsupervised Hierarchical Sparse Coding algorithm inspired by Biological Vision
The brain has to solve inverse problems to correctly interpret sensory data and infer the set of causes that generated the sensory …
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Unsupervised learning applied to robotic vision
see a related work describing SDPC in: Victor Boutin, Angelo Franciosini, Frédéric Chavane, Franck Ruffier, Laurent U Perrinet (2021). Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system.
2017-11-24
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Controlling an aerial robot with human gestures using bio-inspired algorithm
Improve performances of existing recognition computer vision algorithms with biological concepts. The gain are expected in the …
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Efficient learning of sparse image representations using homeostatic regulation
One core advantage of sparse representations is the efficient coding of complex signals using compact codes. For instance, it allows …
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Efficient learning of sparse image representations using homeostatic regulation
One core advantage of sparse representations is the efficient coding of complex signals using compact codes. For instance, it allows …
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