README
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wwi3d
wwi3d does deep predictive learning of 3D objects tumbling through space, with periodic saccadic eye movements, providing plenty of opportunity for prediction errors. wwi = what, where integration: both pathways combine to predict object -- where (dorsal) pathway is trained first and residual prediction error trains what pathway.
This is (an updated version of) the model described in:
- O’Reilly, R. C., Russin, J. L., Zolfaghar, M., & Rohrlich, J. (2020 / in press). Deep Predictive Learning in Neocortex and Pulvinar. Journal of Cognitive Neuroscience, ArXiv:2006.14800 [q-Bio]. http://arxiv.org/abs/2006.14800
Install
See Emergent Wiki Install page for installation instructions -- basically you need install Go (e.g., brew install go on mac), then do go build in this directory.
Then, you need to get CU3D100_20obj8inst_8tick4sac.tar from this google drive folder, which has the 3D rendered movies that the network is trained on. Install it as images in the directory where this code is. For example:
$ tar -xf CU3D100_20obj8inst_8tick4sac.tar
$ mv CU3D100_20obj8inst_8tick4sac images
(we usually have it in a centralized place and create a symbolic link, which works on the cluster too..)
Running
Just run the wwi3d executable that is built with the go build command. You can see how it processes processes input patterns, etc. It takes about 1 day to train across 32 processors on our older cluster (use go build -tags mpi to build with mpi support), so it would take about 16 days without MPI. Threading has decreasing benefits but is quite efficient for 2 threads, which is what it is configured for.
Documentation
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Overview ¶
wwi3d does deep predictive learning of 3D objects tumbling through space, with periodic saccadic eye movements, providing plenty of opportunity for prediction errors. wwi = what, where integration: both pathways combine to predict object -- *where* (dorsal) pathway is trained first and residual prediction error trains *what* pathway.
Source Files