31–40 of 40 posts
There's three things I've always been baffled by the lack of interest in the current deep learning based AI field when it comes to parallels with biological brain: 1. Biological plausibility of back prop. 2. The lack of interest/consideration of time-continuous input on network. They are currently discrete and "learning" and inference is done separately. That's not how most organisms work. 3. The lack of consideratio…
Seems to me we should be training DL networks to adjust models to resemble models trained via backprop, but without access to backprop, and see what kinds of heuristics it provides.
There's three things I've always been baffled by the lack of interest in the current deep learning based AI field when it comes to parallels with biological brain: 1. Biological plausibility of back prop. 2. The lack of interest/consideration of time-continuous input on network. They are currently discrete and "learning" and inference is done separately. That's not how most organisms work. 3. The lack of consideratio…
not an expert (more like a noob) by any means but: 1) from neuroscience point of view you have cortical columns with layers that are wired to send the input forward but to also propagate feedback. the layers constantly predict what is going to happen (by having neurons fire) and usually it’s the delta between what is predicted and what is coming from the sensory system that drives the reinforcement or the weakening o…
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You don't think setting a reminder in your calendar for 2 years from now would suffice?
It's too much clicks away, it's should be a matter of one click
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not an expert (more like a noob) by any means but: 1) from neuroscience point of view you have cortical columns with layers that are wired to send the input forward but to also propagate feedback. the layers constantly predict what is going to happen (by having neurons fire) and usually it’s the delta between what is predicted and what is coming from the sensory system that drives the reinforcement or the weakening o…
re 2: are you saying that the amount of signal isn't continuous, or that the time of the signal is not continuous? If the latter, I'm not sure why being made of particles would prevent the signal from being continuous in time. Err, not "a continuous function of time", just in the sense of "not a discrete-in-time thing", unlike things on a computer that are synced with a global clock cycle.
also as a side-note, discrete-in-time does not imply a global clock cycle
Perhaps more surprisingly the mentioned ‘advances’ are not cited!
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re 2: are you saying that the amount of signal isn't continuous, or that the time of the signal is not continuous? If the latter, I'm not sure why being made of particles would prevent the signal from being continuous in time. Err, not "a continuous function of time", just in the sense of "not a discrete-in-time thing", unlike things on a computer that are synced with a global clock cycle.
the amount. also as a side-note, discrete-in-time does not imply a global clock cycle
In the case of the amount of signal being discrete because it is made of particles, this is also true of the floats that computer neural nets are computing with anyway, isn't it?
I think the top level comment (by NalNezumi) was talking about continuous-ness in time anyway, (and, I think the strength of firing of biological neurons is thought to be at least approximately binary anyway? not sure about that.) so I'm not sure I see the bearing of them being unable to be truly continuous in strength on the question?
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the amount. also as a side-note, discrete-in-time does not imply a global clock cycle
Thank you for the correction regarding discrete-in-time. I appreciate it. In the case of the amount of signal being discrete because it is made of particles, this is also true of the floats that computer neural nets are computing with anyway, isn't it? I think the top level comment (by NalNezumi) was talking about continuous-ness in time anyway, (and, I think the strength of firing of biological neurons is thought to…
Earlier quoted context omitted.
You're downvoted but this is correct. It is much like when the analogy was "springs and cogs", and an academic department created in that era "cog-nitive" science, would be the attempt to rotate enough gears in the right way. Many presumptions are being made here in "computational cognitive science" which preclude including many relevant features of animal learning and animal biology. Their whole world view is that "…
> Their whole world view is that "patterns of electrical signals in neurons" is where learning takes place Actually, the mechanisms are chemical processes involving trophic factors (i.e. inputs to those processes) and alteration of the physical structures the signals are transmitted with. You say "the brain grows" but the alteration of its structure to strengthen our weaken transmission and connections in response to…
It is a hypothesis, and I think a false one, that this is identical to learning.
The dynamical number and arrangement of neuronal tissues is not incidental.
The micro (sub-neurone), medial (neurone) and macro structure (morphology) of the brain is time-varying, not merely its connection patterns (conditioned on fixed micro/medial/macro).