Just $225 to read the actual paper... what a steal! If someone would so kindly share that pdf on here I'd greatly appreciate it.
Nvm found it: https://www.biorxiv.org/content/early/2016/12/28/097170
The hippocampus as a 'predictive map'
21–30 of 54 posts
Re: The hippocampus as a 'predictive map'
#22Just $225 to read the actual paper... what a steal! If someone would so kindly share that pdf on here I'd greatly appreciate it. Nvm found it: https://www.biorxiv.org/content/early/2016/12/28/097170
ah well good catch
usually I try digging the authors in case preprints are on their own webpage
and just in case people miss it, the latest version is also there https://www.biorxiv.org/content/early/2017/07/27/097170
Re: The hippocampus as a 'predictive map'
#23Reading about rewards in AI context makes me wonder, why current AI only focuses on the network topology aspect of natural intelligence. What about the neurotransmitters like dopamine or serotonine? Could they (or more specific: their effects) be key to an AI that feels more “natural“?
Current AI approaches don't have that much to do with the biological brain. Despite the name 'neuronal networks' that we have history to thank for, it's better think of them as just long compositions of functions with a lot of parameters one can tweak. Historically researchers looked at activation functions that are at least somewhat justified by nature, like the sigmoid. More recently people have focused on the math…
Actually ReLU(x) = max(x,0) can be thought after a change of parameters as the firing rate response of a leaky integrate and fire neuron at least for a certain range of parameters. So it is not completely far fetched, but of course nature does not use rate coding exclusively.
Re: The hippocampus as a 'predictive map'
#24Earlier quoted context omitted.
Downvoters please explain what's so stupid about my question.
I didn't downvote, but I have an inkling. I'll share what some of these people may be thinking about your comment. I'll be hyper-critical there because you specifically asked about why someone might have downvoted, so don't take it personally. "Reading about rewards in AI context makes me wonder, why current AI only focuses on the network topology aspect of natural intelligence." Well, it doesn't. Not at all. It look…
If someone only has familiarity with AI from reading passing news stories, it is reasonable to think they would have such a perception of the field. I think your diagnosis of the downvoters might be correct, but "not even wrong" seems a bit extreme. AFAIK somebody out there is probably trying to do a research paper on simulated neurotransmitters as we speak. More interesting would be if anyone had links to existing research in that area. It may not be high-efficiency or high-performance technology, but as a learning tool for exploring theoretical models I think it could be very beneficial.
The topic reminds me of a book I read (and was fascinated by at the time), The Muse in the Machine by David Gelernter. It seems he has moved away from AI research and into political punditry, however, and recently made headlines denying that the moon landing ever happened. Nevertheless, that book contained a lot of exposition around the idea of creating AI with simulated emotion. This is simultaneously in line with the idea of simulating neurotransmitter function, but also has parallels with more general mathematical techniques that are in widespread use. Thus, the book (and perhaps the idea itself) are a bit dated, but I don't think it makes them less relevant.
Re: The hippocampus as a 'predictive map'
#25Re: The hippocampus as a 'predictive map'
#26Earlier quoted context omitted.
Downvoters please explain what's so stupid about my question.
I upvoted you, and I'm actually glad you asked the question despite downvotes. I imagine those who did are of the opinion that essentially you're ascribing too much function to the neurotransmitters themselves, as opposed to the contexts in which the presence of the neurotransmitters are used as signals. Take a look at this brain schematic here [0]. I vehemently disagree with some of the presented-as-fact ideological…
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Re: The hippocampus as a 'predictive map'
#27Just $225 to read the actual paper... what a steal! If someone would so kindly share that pdf on here I'd greatly appreciate it. Nvm found it: https://www.biorxiv.org/content/early/2016/12/28/097170
theres a website called sci-hub (which maybe illegal) that you can use to get almost any paper in a STEM field, especially from major journals. you type www.sci-hub.cc/ and then paste the DOI of the paper. it takes you straight to the pdf!
Re: The hippocampus as a 'predictive map'
#28Reading about rewards in AI context makes me wonder, why current AI only focuses on the network topology aspect of natural intelligence. What about the neurotransmitters like dopamine or serotonine? Could they (or more specific: their effects) be key to an AI that feels more “natural“?
Current AI approaches don't have that much to do with the biological brain. Despite the name 'neuronal networks' that we have history to thank for, it's better think of them as just long compositions of functions with a lot of parameters one can tweak. Historically researchers looked at activation functions that are at least somewhat justified by nature, like the sigmoid. More recently people have focused on the math…
> Despite the name 'neuronal networks' that we have history to thank for, it's better think of them as just long compositions of functions with a lot of parameters one can tweak.
Actually, the name is not so misleading because `ReLU(Wx + b)` is an approximation of the response of a large ensemble of integrate-and-fire neurons in terms of the firing rate [1] (it models populations of pyramidal neurons in the cortex). The ReLU activation function basically approximates a logarithmic activation function [2]. Obviously many kinds of computations are left out in this model, for example in pyramidal neurons, the integration of the incoming spikes is often sublinear rather than linear; the branches of the neurons that receive signals (dendrites) can themselves perform complex computations such as spatial clustering and logical operators basically sending small (dendritic) spikes forward and backward within an neuron [3]; short-term plasticity is left out entirely and so are countless types of interneurons which inhibit other neurons nearby and measure mean activity across populations of neurons etc.
[1] https://web.stanford.edu/group/brainsinsilicon/documents/Won...
[2] https://www.utc.fr/~bordesan/dokuwiki/_media/en/glorot10nips...
[3] https://neurophysics.ucsd.edu/courses/physics_171/annurev.ne...
Re: The hippocampus as a 'predictive map'
#29Earlier quoted context omitted.
Current AI approaches don't have that much to do with the biological brain. Despite the name 'neuronal networks' that we have history to thank for, it's better think of them as just long compositions of functions with a lot of parameters one can tweak. Historically researchers looked at activation functions that are at least somewhat justified by nature, like the sigmoid. More recently people have focused on the math…
Actually ReLU(x) = max(x,0) can be thought after a change of parameters as the firing rate response of a leaky integrate and fire neuron at least for a certain range of parameters. So it is not completely far fetched, but of course nature does not use rate coding exclusively.
Yes. Though I think they stumbled on ReLU just from technical considerations and not taking nature into account?
That happens fairly often in engineering: looking around enough one can often find natural precedents for our artifices. But directly trying to copy nature did not lead to the plane or helicopter.
Re: The hippocampus as a 'predictive map'
#30Earlier quoted context omitted.
I didn't downvote, but I have an inkling. I'll share what some of these people may be thinking about your comment. I'll be hyper-critical there because you specifically asked about why someone might have downvoted, so don't take it personally. "Reading about rewards in AI context makes me wonder, why current AI only focuses on the network topology aspect of natural intelligence." Well, it doesn't. Not at all. It look…
If someone only has familiarity with AI from reading passing news stories, it is reasonable to think they would have such a perception of the field. I think your diagnosis of the downvoters might be correct, but "not even wrong" seems a bit extreme. AFAIK somebody out there is probably trying to do a research paper on simulated neurotransmitters as we speak. More interesting would be if anyone had links to existing r…
IIRC there was at least some work done in the 80's and 90's about neural networks where neurons were located in a 2- or 3-D space and could emit "chemicals" that would "diffuse" through the simulated environment and influence neurons' behaviour depending on local concentration. Nothing new here, but maybe the idea is worth another look. Wouldn't be the first time an old idea turns out to be a really good one decades after it's first inception. It might be that the ground is now more fertile for this than three decades ago.
Still, please note that the critiqued comment did not even go as far as to even suggest an idea about the mechanisms involved beyond (implicitly, not even explicitly) "somewhat like in the brain". There's just not enough meat there to even do a proper critique, and on this basis I pulled the "Not even wrong"-card, which is of course at bit harsh, but that was the whole point about trying to put myself into the position of a downvoter.