[HN Gopher] Nanowire Synapses 30,000x Faster Than Nature's
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Nanowire Synapses 30,000x Faster Than Nature's
Author : TeacherTortoise
Score : 41 points
Date : 2022-10-28 20:09 UTC (2 hours ago)
(HTM) web link (spectrum.ieee.org)
(TXT) w3m dump (spectrum.ieee.org)
| gardenfelder wrote:
| 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?
|
| [1] https://www.jneurosci.org/content/39/15/2792
| orbifold wrote:
| 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.
| a-dub wrote:
| spiking neural networks are interesting though, and new
| computational substrates that allow for experiments at larger
| scales could produce some interesting results.
|
| today's sum n' squash (sometimes not even squash) graph
| networks were just kind of a curiosity before gpus turned
| them into a new very successful computational paradigm. maybe
| we'll see something similar with these high element count
| optical spiking graphs, even if they aren't great
| approximations of the real biology.
|
| i like to think that a new analog computational substrate (or
| mixed analog and digital system) will be what drives the next
| leap in machine computation.
| superkuh wrote:
| 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
| ad404b8a372f2b9 wrote:
| Can you elaborate on how the axo-axonal transmissions blow up
| our simple models? I couldn't understand anything from that
| summary.
|
| Would it be the equivalent of edges communicating between
| each other in artifical neural networks?
| bismuthcrystal wrote:
| Biologists just won't allows us to have any fun. It is always
| this kind of rhetoric: "what, are you modeling the brain
| without considering the influence of <insert obscure type of
| cell> on the hormone regulated blood flow around ion pump
| circuits during chinese new year neuron firing patterns? you
| are obviously bounded to fail..."
|
| This whole AI field keeps on failing because people like to
| overthink things. Did Michelangelo need to know molecular
| chemistry to make sculptures? Why do people pretend there is
| no artistic component to building AI? Rant finished.
| bee_rider wrote:
| I don't think this is the case; the whole field of AI seems
| pretty healthy at the moment, not failing, and not all that
| worried about the inaccuracy of their model (It's only a
| model /Patsy -- but it can still host the whole song-and-
| dance).
| mdp2021 wrote:
| > _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.
|
| --
|
| 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.
| bee_rider wrote:
| We should also note that the article only references the
| over-dramatic comparison to biological neurons in passing in
| the first paragraph (wonder if it is an "author gets it, but
| doesn't get to pick the title" type problem).
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