21–30 of 35 posts
Everyone complaining or fussing about how primitive this model is on a browser, ya'll need to chill out and stop taking beautiful work like this for granted. Some of you may say this is easy to do or why didn't he also "do this" or "do that" - how about you help out and make it better ;) @sethbannon Thanks for the visuals and hard work!
Everyone complaining or fussing about how primitive this model is on a browser, ya'll need to chill out and stop taking beautiful work like this for granted. Some of you may say this is easy to do or why didn't he also "do this" or "do that" - how about you help out and make it better ;) @sethbannon Thanks for the visuals and hard work!
There is nothing to do better because better is doing real simulation and we are far from it. https://www.humanbrainproject.eu/
Is a neural network all that you would need to model in order to simulate a human's thoughts? How far are we from mapping a real brain to a simulation like this and hitting play?
However, I have seen estimates that somewhere around a yottahert class computer working for a few years might be capable of simulating a brain sufficiently complex to hold an arbitrary conversation. Building the software capable of running such a simulation is another story.
Really Impressive. I wonder if you could use this to visualize a neural net that's solving actual problems.
This doesn't look like a neural network that's solving any actual problems beyond visualizing firing patterns. Most ANNs don't have any concept of time built into the model. When you're in feed-forward mode, the underlying computation is simply a bunch of dot products. However, you might be able to make some cool visualizations if your ANN was a Spiking Neural Network. [1] https://en.wikipedia.org/wiki/Spiking_neural…
Would it be useful though? Maybe. Probably not as the fact that a neuron fires isn't nearly as useful as why it fired (what it represents).
But I like that you mention time. It always seemed odd that NN's today completely ignore that aspect. Sure whether a neuron fires is binary. But the accumulation of spikes is not binary and highly temporal. yet we completely ignore this aspect.
Is it based on anything or just some random connection and nice visualization (which then isn't really impressive)
Is it based on an actual model of neural activity (Izhikevich, H-H, etc.) or is it an abstract visualisation in the sense that it just looks like neurons firing?
OK, it's the latter. Pretty pictures, moving on.
Everyone complaining or fussing about how primitive this model is on a browser, ya'll need to chill out and stop taking beautiful work like this for granted. Some of you may say this is easy to do or why didn't he also "do this" or "do that" - how about you help out and make it better ;) @sethbannon Thanks for the visuals and hard work!
Earlier quoted context omitted.
This doesn't look like a neural network that's solving any actual problems beyond visualizing firing patterns. Most ANNs don't have any concept of time built into the model. When you're in feed-forward mode, the underlying computation is simply a bunch of dot products. However, you might be able to make some cool visualizations if your ANN was a Spiking Neural Network. [1] https://en.wikipedia.org/wiki/Spiking_neural…
Well it's thresholded so you could visualize the "activated" neurons in a manner such as this. Would it be useful though? Maybe. Probably not as the fact that a neuron fires isn't nearly as useful as why it fired (what it represents). But I like that you mention time. It always seemed odd that NN's today completely ignore that aspect. Sure whether a neuron fires is binary. But the accumulation of spikes is not binary…
Earlier quoted context omitted.
Well it's thresholded so you could visualize the "activated" neurons in a manner such as this. Would it be useful though? Maybe. Probably not as the fact that a neuron fires isn't nearly as useful as why it fired (what it represents). But I like that you mention time. It always seemed odd that NN's today completely ignore that aspect. Sure whether a neuron fires is binary. But the accumulation of spikes is not binary…
I'm confused by you guys saying NN's today completely ignore time? If I have a network of Hodgkin-Huxley neurons and I integrate the equations, I'm integrating over time. I'm not sure how that is ignoring it? Disclaimer: it has been about 4 years since I've written any code to integrate H-H neurons, so I might be forgetting something really obvious. :) But I can't imagine EVERYONE is ignoring time when they simulate…
In a machine learning neural network there is no integration; they are basically just nonlinear data transformations that can (usually...) be trained.
Everyone complaining or fussing about how primitive this model is on a browser, ya'll need to chill out and stop taking beautiful work like this for granted. Some of you may say this is easy to do or why didn't he also "do this" or "do that" - how about you help out and make it better ;) @sethbannon Thanks for the visuals and hard work!
I agree, a really cool project which I would imagine could be extended to a "real" model without a ton of work. I imagine the amount of work to fork a javascript library which integrated a network of H-H model neurons to make it output this type of visualization would be far more work than adding the H-H piece to this code. I could be wrong but at first blush, that's how I see it.
This is because the visualization is basically just a graph, with axons as edges and neurons as nodes, and then signals are propagated with extremely simple graph-traversal style logic. On the other hand, the H-H model tries to deal with the physical reality of the human brain so it is much more complicated and relies on some data (like axon width) that simply doesn't exist here.
It would be possible and potentially interesting, for sure, but probably more work than you might think.