“Modular Brain AUNNs for Uploads”, Gwern2023-05-22 (MLP NN, neuroscience, brain imitation learning)⁠:

Proposal for applying the AUNN neural net architecture to reconstruction of brains in a modular piece-wise fashion.

Emulating an entire brain from scratch using a single monolithic AUNN is probably too hard.

We can instead continue the previous Herculaneum papyri discussion of embeddings & constraints and propose a modularized brain, where each module is an AUNN instance, communicating embeddings with other AUNNs. These AUNNs are then collectively trained to reconstruct both the raw data of their region, to learn local algorithms, but also reconstruct global metadata like coarse EEG signals or functional connectivity, to emulate overall activity.

The modularization helps with tractability, but also enables progressive replacement of AUNN units with any more biologically-plausible simulations, and can help prioritize what brain regions are of most scientific value to scan, conserving limited scanning resources.

With sufficiently good brain AUNNs, this may even enable upload of specific individuals by scanning the minimum possible brain regions which functionally distinguish individuals.

• Challenges
• Modularized AUNNs
• Cross-AUNN Embeddings
• Global Losses
• Progressive Uploading
  • Few-Shot Minds