coproc-poc

Proof of concept of a neural co-processor, based on a simulated brain

Layout

• train_cpn.ipynb: contains a hook to start training a CPN+EN on the simulated brain, with a default choice of lesion design and observation function

• The other jupyter notebooks are bit rotted: they are exploratory, and contain some prior implementations of what is below

• experiment: contains the simulated "brain", which is a pytorch implementation of a network from Michaels et al.

  • mRNN.py - contains the actual Michaels "modular" RNN, some hooks to add a lesion and observation function, and a dataset loader. The data comes from Michaels directly.
  • experiment.py - controls the flow of a training session. It allows you to define your own model relatively easily by subclassing 'CoProc'. That model can be hooked in as you see in the jupyter notebook above.
  • lesion.py - some lesion designs. These are pluggable, so one can design the lesion in various ways. I'm not sure how much that has bit rotted at this point.
  • michaels_load.py - helper to load the Michaels training data as well as network weights.
  • observer.py - observation functions; again these are pluggable and tunable
  • stim.py - the stimulation model. As before: this is also pluggable.
  • config.py - used by the experment to bind the lesion, observation function, etc. to specific instances

• cpn.py - the CoProc defining the coprocessor design you saw in the slides: a CPN/EN approach that uses error backprop

• cpn_model.py - defines the networks we use for the CPN

• stim_model.py - defines the networks we use for the EN

• cpn_epoch_* - these have the forward, backward, and stats tracking passes that cpn.py uses for training the CPN and EN

• micheals: a small subset of the data we received from Michaels, referenced by e.g. michaels_load.py above