Sparse coding model for V1 receptive fields
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- UC Davis,Cornell University
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 1 December 1997
- Authors
- Bruno A. Olshausen, David J. Field
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Miscellaneous image analysis
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 28,800,000 tokens
In Simulation Methods: "The data for training were taken from ten 512 × 512 pixel images of natural surroundings"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 4,257
Sources
Where this record came from and when it was last checked.
- Reference
- Sparse coding with an overcomplete basis set: A strategy employed by V1?
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Sparse coding model for V1 receptive fields was published by UC Davis,Cornell University, in United States of America, in December 1997. academia,Academia is the category the publisher falls under.
It works in Vision, and is recorded as doing miscellaneous image analysis.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training set ran to roughly 28,800,000 tokens.
Answers
Sparse coding model for V1 receptive fields — common questions
What GPU do I need to run Sparse coding model for V1 receptive fields?
None. Sparse coding model for V1 receptive fields is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Sparse coding model for V1 receptive fields open source?
The licensing for Sparse coding model for V1 receptive fields was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does Sparse coding model for V1 receptive fields have?
No parameter count has been published for Sparse coding model for V1 receptive fields, which is why no memory or speed figure appears on this page.
Who created Sparse coding model for V1 receptive fields?
Sparse coding model for V1 receptive fields was published by UC Davis,Cornell University, based in United States of America, categorised as academia,Academia.
When was Sparse coding model for V1 receptive fields released?
Sparse coding model for V1 receptive fields was published in December 1997. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Sparse coding model for V1 receptive fields used for?
Sparse coding model for V1 receptive fields works in Vision, and is recorded as handling miscellaneous image analysis. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.