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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>An Exact Mapping From ReLU Networks to Spiking Neural Networks · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2212.12522/</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/2212.12522/feed.xml" rel="self" type="application/rss+xml" /><item><title>An Exact Mapping From ReLU Networks to Spiking Neural Networks — mentioned by jegp</title><link>https://news.ycombinator.com/item?id=35697321</link><guid isPermaLink="false">urn:hackerpapers:mention:2212.12522:35697321</guid><pubDate>Tue, 25 Apr 2023 06:54:00 GMT</pubDate><description>&lt;p&gt;If we think about spikes as discretized transfer functions, I would say it&amp;#x27;s totally viable to &amp;quot;hack&amp;quot; them to represent numerical approximations. In fact, a recent paper demonstrates that exact mappings between ANNs and SNNs exist: https://arxiv.org/abs/2212.12522 There are some pitfalls here, though, and I&amp;#x27;m biased against these kinds of methods because they don&amp;#x27;t use the temporal traces of the neuron integration. Regarding RWKV, someone actually trained a &amp;quot;SpikeGPT&amp;quot;: https://arxiv.org/abs/2302.13939 That&amp;#x27;s a neat insight, which will be great for porting these models onto energy-efficient…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2212.12522/"&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>
