I found this[1] article to give a nice overview over spiking neural networks and their connections to the more &quot;traditional&quot; neural networks of modern fame.In particular the connection between the typical weighted-sum plus activation function and a simplistic spiking model where one considers the output simply by the spiking rate was illuminating (section 3).[1]: <a href="https:&#x2F;&#x2F;www.ncbi.nlm.nih.gov&#x2F;pmc&#x2F;articles&#x2F;PMC9313413&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.ncbi.nlm.nih.gov&#x2F;pmc&#x2F;articles&#x2F;PMC9313413&#x2F;</a> Spiking Neural Networks and Their Applications: A Review

Context: Sorbet is also the name of a popular Ruby type checker[1], built by Stripe.[1]: <a href="https:&#x2F;&#x2F;sorbet.org" rel="nofollow">https:&#x2F;&#x2F;sorbet.org</a>

Can’t wait for the follow up paper, RBS

I wonder how well this model can typecheck Ruby code.

Thank you very much for posting this! The code is indeed there, that&#x27;s great.

They may not have linked the source in the paper, but it&#x27;s not hard to find: <a href="https:&#x2F;&#x2F;github.com&#x2F;Kaiwen-Tang&#x2F;Sorbet">https:&#x2F;&#x2F;github.com&#x2F;Kaiwen-Tang&#x2F;Sorbet</a>Haven&#x27;t checked if there&#x27;s enough there to build it.

The ML community has historically held themselves to a higher standard, IME.

Results get reproduced without code or weights all the time. I note that in this case training data and evaluation benchmarks are public.

There&#x27;s no code or weights released =&gt; no way to reproduce their results.

I wonder how far we will move the goalposts once we have a multimodal transformer type model running on neuromorphic hardware.

neuromorphic hardware is just hardware that has biologically inspired designs.spiking neural networks are artificial neural networks that actually simulate the dynamics of spiking neurons. rather than sums, ramps and squashing, they simulate actual spike trains and the integration of energy that occurs in the dendrites.neuromorphic hardware can range from specialized asics for doing these simulations efficiently to more experimental hybrid analog-digital systems that use analog elements to do more of the computation.it&#x27;s all very cool stuff, but i tend to think of snns as similar to the wings on the avion 3 where simplified unit functions look more like a modern jet wing.but who knows, maybe the neuromorphic route will open the door to far more efficient computations. personally, i&#x27;m very excited about potential wins that could come from novel computational substrates!

To be fair, all words are made up.Words are useful to the extent they effectively communicate with the intended audience.This can be accomplished by a mix of familiarity (has this word been already used enough in the target audience with the intended meaning) and the ability to evoke new meanings by intuitive derivation rules (word composition, affixes, ...)In the case of this title, fwiw, it was perfectly clear to me what this was about because I&#x27;m already familiar with related topics and they were using the same terminology

And even with a willingness to make up words, it’s STILL hard to name tech projects uniquely: <a href="https:&#x2F;&#x2F;github.com&#x2F;sorbet&#x2F;sorbet">https:&#x2F;&#x2F;github.com&#x2F;sorbet&#x2F;sorbet</a>

sometimes it seems folks are just making up words.

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I definitely know what &quot;A&quot; and &quot;model&quot; means.

Sorbet: A neuromorphic hardware-compatible transformer-based spiking model