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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A Unified, Scalable Framework for Neural Population Decoding · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2310.16046/</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/2310.16046/feed.xml" rel="self" type="application/rss+xml" /><item><title>A Unified, Scalable Framework for Neural Population Decoding — mentioned by rkp8000</title><link>https://news.ycombinator.com/item?id=43373945</link><guid isPermaLink="false">urn:hackerpapers:mention:2310.16046:43373945</guid><pubDate>Sat, 15 Mar 2025 17:32:24 GMT</pubDate><description>&lt;p&gt;There are a few folks working on this in neuroscience, e.g. training transformers to &amp;quot;decode&amp;quot; neural activity (https://arxiv.org/abs/2310.16046). It&amp;#x27;s still pretty new and a bit unclear what the most promising path forward is, but will be interesting to see where things go. One challenge that gets brought up a lot is that neuroscience data is often high-dimensional and with limited samples (since it&amp;#x27;s traditionally been quite expensive to record neurons for extended periods), which is a fairly different regime from the very large data sets typically used to train LLMs, etc.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2310.16046/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
