The piece is chock full of interesting findings, using terms biologists routinely use. But, do those terms they use, e.g. neuron, synapse mean the same thing they do for biologists? For instances, we know that synapses can be one of excitatory or inhibitory, and we know that neurons are bathed in a wash of hormones. Neurons make hormones which serve other functions throughout the brain. For instance "Neuron-Derived Estrogen Regulates Synaptic Plasticity and Memory" [1]. How does the linked work stack up against that?
Short answer is no. The field is full with tenuous analogies. Then again „Neural Networks“ are also at best metaphorically related. More accurate existing Neuron models are actually also plagued by lots of limitations among them that they are typically implemented in 3 ancient domain specific languages with lots and lots of hardcoded constants copied from research papers.
> But, do those terms they use, e.g. neuron, synapse mean the same thing they do for biologists
They are not meant to. This is not "brain simulation" or similar - which exists, but is a different matter. This context is instead about neuromorphic computing, as hardware implementation of components for Artificial Neural Networks. And results seem to be remarkable:
> They calculated that the synapses are capable of spike rates exceeding 10 million hertz while consuming roughly 33 attojoules of power per synaptic event (an attojoule is 10-18 of a joule)
The comparison with biological neuro-transmission is just indicative - for trivia, for curiosity.
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Edit:
on the contrary, these devices aim to be in a way simpler than ANN's neurons (far from aiming to be as complex as cerebral neurons):
> By only rarely firing spikes, these devices shuffle around much less data than typical artificial neural networks and, in principle, require much less power and communication bandwidth
That is because the underlying aim is to achieve using a single photon for communication, with an immediate potential practical use in ANNs.
All that, more, and it seems like every computational biology analogy just completely forgets about the most common cell type in the brain: astrocytes. And then there are things like axo-axonal transmission that totally blow up the simple models, https://www.cell.com/neuron/fulltext/S0896-6273(22)00656-0
I know these don't map 1-to-1 on real natural synapses, but let's say you can magically replace all your natural synapses in a brain with this. What would be the experienced effect? Would you be able to think faster, or would perceived time slow down? Or something else?
I wonder if a human's "refresh rate" has been evolutionary optimized for its environment. Maybe there's no need to evaluate things 30000x faster if our bodies can't react that fast. I guess it would make for faster reaction times, e.g. when driving: driver in front of you hits the brakes, the vision signal enters your brain with the same delay (are optic nerves synapses too?), but I guess the difference is your brain would be able to process the meaning of the change (the red lights coming on, the car getting larger in your vision) a lot faster.
The brain then sends a signal for the foot to react, but the amount of time it will take for the foot to move will be the same as normal since the signal takes the same time to reach the foot. I imagine at 30000x, you would notice that it's taking very very long for the foot to move. To notice it would mean to have feedback from the skin inside your socks/pants that the foot/leg is moving against your sock/pants, and the brain will probably think that feedback is taking forever after it sent the signal.
Imagine a game world, where all your opponents 30000 times slower, 30000 times less energy efficient. Time and energy central resources in this game, which cannot be faked anyhow. Even if nuclear fusion or antimatter energy sources are everywhere.
Not exactly impressive, nature is famous for calling it quits once something works well enough. Anyone who wants to can create a non-biological neuron/axion connection that runs orders of magnitude faster. The real question is "but does it actually do something" because that's where nature shines pretty well.
Yes, evolution builds upon what went before rather than starting fresh, but nature never calls it quits. It's a process, not a thinking entity. In any stable population there will be variances that have neither a benefit or cost until environmental pressures force it to "select" the most appropriate. You have to look at a longer timescale to see the adaptations take hold.
You could say species dying out is calling it quits in a way, but evolution encompasses everything not just the extinct - but I don't think that's what you meant.
> ...nature is famous for calling it quits once something works well enough...
Great, so we're just bags of meat sashaying around with loads of technical debt baggage via the slapdash coding delivered by nature in a multi-billion year project. The ongoing result of always picking good and cheap over fast.
I'm not sure whatever for they need such nanowire synapses, because in real/wet neural networks the timings of neural firing, delay and latency is as essential as the synapse itself. How fast a wet synapse works is modulated, some are isolated from modulation. I don't think a new synthetic synapse being 30.000x faster will do anything good if the whole system is not up to par with a wet neural system.
gardenfelder|3 years ago
[1] https://www.jneurosci.org/content/39/15/2792
orbifold|3 years ago
mdp2021|3 years ago
They are not meant to. This is not "brain simulation" or similar - which exists, but is a different matter. This context is instead about neuromorphic computing, as hardware implementation of components for Artificial Neural Networks. And results seem to be remarkable:
> They calculated that the synapses are capable of spike rates exceeding 10 million hertz while consuming roughly 33 attojoules of power per synaptic event (an attojoule is 10-18 of a joule)
The comparison with biological neuro-transmission is just indicative - for trivia, for curiosity.
--
Edit:
on the contrary, these devices aim to be in a way simpler than ANN's neurons (far from aiming to be as complex as cerebral neurons):
> By only rarely firing spikes, these devices shuffle around much less data than typical artificial neural networks and, in principle, require much less power and communication bandwidth
That is because the underlying aim is to achieve using a single photon for communication, with an immediate potential practical use in ANNs.
superkuh|3 years ago
dr_dshiv|3 years ago
POiNTx|3 years ago
netsharc|3 years ago
The brain then sends a signal for the foot to react, but the amount of time it will take for the foot to move will be the same as normal since the signal takes the same time to reach the foot. I imagine at 30000x, you would notice that it's taking very very long for the foot to move. To notice it would mean to have feedback from the skin inside your socks/pants that the foot/leg is moving against your sock/pants, and the brain will probably think that feedback is taking forever after it sent the signal.
miovoid|3 years ago
IIAOPSW|3 years ago
TheRealPomax|3 years ago
TeacherTortoise|3 years ago
Yes, evolution builds upon what went before rather than starting fresh, but nature never calls it quits. It's a process, not a thinking entity. In any stable population there will be variances that have neither a benefit or cost until environmental pressures force it to "select" the most appropriate. You have to look at a longer timescale to see the adaptations take hold.
You could say species dying out is calling it quits in a way, but evolution encompasses everything not just the extinct - but I don't think that's what you meant.
yourapostasy|3 years ago
Great, so we're just bags of meat sashaying around with loads of technical debt baggage via the slapdash coding delivered by nature in a multi-billion year project. The ongoing result of always picking good and cheap over fast.
miovoid|3 years ago
thrown_22|3 years ago
visarga|3 years ago
raziel2701|3 years ago
dschuetz|3 years ago
yarg|3 years ago
How densly is it possible to structure them and still provide adequate cooling?
incrudible|3 years ago