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Nicole Rust

Following on the idea that the theories we will need to tackle the complexity of the brain have not been developed yet (e.g. mastodon.social/@NicoleCRust/1)

What types of up and coming theoretical(ish) frameworks are you most excited about? Dynamical systems / RNNs? Topology? Network theory? Something else entirely?

@complexsystems @cogneurophys @PessoaBrain @SussilloDavid @carlosbrody @Neurograce @neuralreckoning @tyrell_turing @DrYohanJohn @cian @WiringtheBrain @tdverstynen @neuralengine (Anyone?)

MastodonNicole Rust (@NicoleCRust@mastodon.social)I'm eager for the brain research community to discuss how we plan to tackle the immense complexity of the brain. Here's a summary of how I envision that we'll go about it, written with complexity guru Shaul Druckmann: Journal link: https://direct.mit.edu/jocn/article-abstract/doi/10.1162/jocn_a_01950/114112/Unraveling-the-Entangled-Brain-How-Do-We-Go-About?redirectedFrom=fulltext Website: https://www.sas.upenn.edu/psych/rust-lab/DruckmannRust_Commentary_final.pdf This is part of a Journal of Cognitive Neuroscience series inspired by @PessoaBrain, and an article summarizing his new book, The Entangled Brain. Link to the article here: https://direct.mit.edu/jocn/article-abstract/doi/10.1162/jocn_a_01908/112783/The-Entangled-Brain?redirectedFrom=fulltext
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Albert Cardona

@NicoleCRust @complexsystems@a.gup.pe @cogneurophys@a.gup.pe @PessoaBrain @SussilloDavid @carlosbrody @Neurograce @neuralreckoning @tyrell_turing @DrYohanJohn @cian @WiringtheBrain @tdverstynen @neuralengine

I find Kim & Bassett 2022 "A Neural Programming Language for the Reservoir Computer" arxiv.org/abs/2203.05032 rather promising. In part because is showing brains are both modular and recursive as hell, which fits well.

Figure 2 of Kim & Bassett 2022 showing how to program matrix operations and solving equations with feedback loops.
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Albert Cardona

@NicoleCRust @complexsystems@a.gup.pe @cogneurophys@a.gup.pe @PessoaBrain @SussilloDavid @carlosbrody @Neurograce @neuralreckoning @tyrell_turing @DrYohanJohn @cian @WiringtheBrain @tdverstynen @neuralengine

There's earlier work that uses the known synaptic weights of cortical circuits to devise recurrent circuits that implement soft winner-take-all and, combining multiple such sWTA modules, construct a state machine.

As described by Rutishauser, Slotine & Douglas in a series of papers scholar.google.com/scholar?hl=

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