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The circuit that lets your brain think and see

83 points by hhs

by w10-1

3 subcomments

by ekelsen

0 subcomment

The actual experiment is basically training a relatively large RNN (1000 units) to do a very simple copy-esque task. The weight values are constrained to be either positive or negative at the beginning of training. The RNN could probably be way smaller and still be made to solve this task.
It isn't even a spiking model.
It seems really hard to go from this experiment to "we've learned anything useful about how brains actually work."
https://www.biorxiv.org/content/10.1101/2023.10.17.562828v2....

by storus

1 subcomments

Why are they using neural nets to model observed behavior (different parts activated) and then applying them to biological neurons that work completely differently? Real neurons communicate using precisely timed spikes and each neuron does a bunch of local computation as well.

by SubiculumCode

0 subcomment

Independent of the research itself, the article makes it seem as if neuroscientist are just discovering the deep recursion all the way back to V1. The idea that this was a one way stream of information processing was discarded a long time ago. Those back projections probably serve lots of functions, but we can be pretty sure they are there to let current context bias the weights for quicker recognition and reaction...e.g. if your context includes snakes, your visual system will attune to recognizing snakes even faster.

by calmbonsai

0 subcomment

by jibal

0 subcomment

"Compare the brain to something like ChatGPT or a large language model. We can do far more, across far more situations, on a tiny fraction of the energy — and without being trained on the whole internet. The brain got there through evolution, through the redundancy built into its wiring. Our models are recurrent neural networks, which are quite different from the transformers behind today's large language models. The goal is to work out these principles one by one and use them to make AI leaner and more adaptive. This inhibition-on-inhibition motif is one of them."
Cool!

by spacebacon

0 subcomment

by juanani

0 subcomment

by yogthos

1 subcomments

Reverse engineering how algorithms in the brain work is a really promising path towards making genuine AI systems which would make the current crop of LLMs obsolete.