3 ms·

here's a free version https://scihubtw.tw/https://www.nature.com/articles/s41593-020-00753-w https://scihubtw.tw/https://www.nature.com/articles/s41593-0... Lo

here's a free version https://scihubtw.tw/https://www.nature.com/articles/s41593-020-00753-w https://scihubtw.tw/https://www.nature.com/articles/s41593-0... Looks like another paper on how everything in the brain can be tied to RNN or GPT2 or some other trendy ML model. 'Cure cancer with deep learning' style. Before something like RNN can be used in a model of biological memory it has to respect the constraints of biological memory. Taking an arbitrary model and twisting the arm of data to fit it makes for some hard sale. Claiming the findings somehow elucidate memory impairments in schizophrenia and autism (which are not even close to being understood), makes it even more look like bad science.

subroutine 6y ago
I agree with this sentiment. To be fair though, the authors did attempt to generate a biologically plausible RNN, which they published in a previous article... https://www.pnas.org/content/pnas/116/45/22811.full.pdf https://www.pnas.org/content/pnas/116/45/22811.full.pdf I'm not fully sold on this model, but the article is fairly dense and I dont fully understand it. (Sejnowski, the senior author, was on my dissertation committee; he fell asleep during my defense lol)
- bra-ket 6y ago
  Oh god, what he calls constraints are the "spiking nature of biological neurons", then he happily proceeds, just like you'd guess, to create this abomination of 'spiking RNN' and train this pretty much plain vanilla RNN via backpropagation, completely disregarding the energy spent, learning mechanism, and actual biological plausibility or relevance of this horrendous architecture as memory model. The first author knows just enough to be dangerous. >Sejnowski fell asleep it surely looks like it
  - subroutine 6y ago
    Dropping a link below in case you're interested - I have a side-project that takes a more literal approach to modeling biologically plausible neural networks (MCMC simulations inside 3D models)... https://github.com/bradmonk/plasticity https://github.com/bradmonk/plasticity
rsiqueira 6y ago
Here's the GitHub with their code for "Working Memory Spiking RNN Model" (This repository provides the code for the model and analyses presented in the paper): https://github.com/rkim35/wmRNN https://github.com/rkim35/wmRNN

subroutine 6y ago

I agree with this sentiment. To be fair though, the authors did attempt to generate a biologically plausible RNN, which they published in a previous article... https://www.pnas.org/content/pnas/116/45/22811.full.pdf https://www.pnas.org/content/pnas/116/45/22811.full.pdf I'm not fully sold on this model, but the article is fairly dense and I dont fully understand it. (Sejnowski, the senior author, was on my dissertation committee; he fell asleep during my defense lol)

bra-ket 6y ago
Oh god, what he calls constraints are the "spiking nature of biological neurons", then he happily proceeds, just like you'd guess, to create this abomination of 'spiking RNN' and train this pretty much plain vanilla RNN via backpropagation, completely disregarding the energy spent, learning mechanism, and actual biological plausibility or relevance of this horrendous architecture as memory model. The first author knows just enough to be dangerous. >Sejnowski fell asleep it surely looks like it
- subroutine 6y ago
  Dropping a link below in case you're interested - I have a side-project that takes a more literal approach to modeling biologically plausible neural networks (MCMC simulations inside 3D models)... https://github.com/bradmonk/plasticity https://github.com/bradmonk/plasticity

rsiqueira 6y ago

Here's the GitHub with their code for "Working Memory Spiking RNN Model" (This repository provides the code for the model and analyses presented in the paper): https://github.com/rkim35/wmRNN https://github.com/rkim35/wmRNN