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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Evolutionary Spiking Neural Networks: A Survey · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2406.12552/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2406.12552/feed.xml" rel="self" type="application/rss+xml" /><item><title>Evolutionary Spiking Neural Networks: A Survey — mentioned by bob1029</title><link>https://news.ycombinator.com/item?id=41232256</link><guid isPermaLink="false">urn:hackerpapers:mention:2406.12552:41232256</guid><pubDate>Tue, 13 Aug 2024 04:30:07 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;d start here: https://arxiv.org/abs/2406.12552 I will say that less is more and &amp;quot;The Bitter Lesson&amp;quot; applies here. Chasing biologically-inspired rabbits, such as STDP and LIF (see paper above/wikipedia), does seem to be a waste of time, especially when we have this thing entirely outside of biology that can arbitrarily replicate, serialize, mutate and simulate billions of instances of the same parent candidate in minutes-hours. Leaky charge carriers and inability to persist learned weights between candidate instantiations are limitations, not features to emulate. Imagine if you could be…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2406.12552/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.NE</category></item></channel></rss>
