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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Towards deep learning with segregated dendrites · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1610.00161/</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/1610.00161/feed.xml" rel="self" type="application/rss+xml" /><item><title>Towards deep learning with segregated dendrites — mentioned by p1esk</title><link>https://news.ycombinator.com/item?id=20078564</link><guid isPermaLink="false">urn:hackerpapers:mention:1610.00161:20078564</guid><pubDate>Sun, 02 Jun 2019 21:59:46 GMT</pubDate><description>&lt;p&gt;Look into capsules, also [1-3] can give you an idea of what can be explored. Evolving NNs [4] would probably benefit the most from NN primitives research. IMO, the best source of ideas for novel primitives is neocortex [5, 6]. [1] https://arxiv.org/abs/1904.03955 [2] https://arxiv.org/abs/1811.11168 [3] https://arxiv.org/abs/1905.02961 [4] https://www.nature.com/articles/s42256-018-0006-z [5] https://arxiv.org/abs/1610.00161 [6] https://arxiv.org/abs/1511.00083&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1610.00161/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.NC</category></item><item><title>Towards deep learning with segregated dendrites — mentioned by boltzmannbrain</title><link>https://news.ycombinator.com/item?id=18511496</link><guid isPermaLink="false">urn:hackerpapers:mention:1610.00161:18511496</guid><pubDate>Thu, 22 Nov 2018 18:12:13 GMT</pubDate><description>&lt;p&gt;There was a rather interesting talk at the Forbes Under 30 summit last month on this, &amp;quot;How The Brain is Inspiring AI&amp;quot; [1], but I cannot find the video. Lavin argued neuro and cognitive science -inspiration, not derivation, is useful at varying levels of abstraction, i.e. Marr&amp;#x27;s levels [2]. He showcased three works at the respective levels: Tenenbaum et al at MIT [3], some Vicarious research [4], and Blake Richards&amp;#x27; work towards &amp;quot;DL with segregated dendrites&amp;quot; [5]. [1] https://www.forbes.com/forbes-live/event/a-i-machine-learnin... [2] http://blog.shakirm.com/2013/04/marrs-levels-of-analysis/…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1610.00161/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.NC</category></item><item><title>Towards deep learning with segregated dendrites — mentioned by zerostar07</title><link>https://news.ycombinator.com/item?id=17187172</link><guid isPermaLink="false">urn:hackerpapers:mention:1610.00161:17187172</guid><pubDate>Wed, 30 May 2018 12:40:39 GMT</pubDate><description>&lt;p&gt;An AI &amp;quot;winter&amp;quot; is a long period in which [edit: funding is cut because...] researchers are in disbelief about having a path to real intelligence. I think that is not the case at this time, because we have (or approaching) adequate tools to rationally dismiss that disbelief. The current AI &amp;quot;spring&amp;quot; has brought back the belief that connectionism may by the ultimate tool to explain the human brain. I mean you can&amp;#x27;t deny that DL models of vision look eerily like the early stages in visual processing in the brain (which is a very large part of it). Even if DL researchers lose their path in search…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1610.00161/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.NC</category></item><item><title>Towards deep learning with segregated dendrites — HN submission</title><link>https://news.ycombinator.com/item?id=12640279</link><guid isPermaLink="false">urn:hackerpapers:submission:1610.00161:12640279</guid><pubDate>Tue, 04 Oct 2016 22:53:45 GMT</pubDate><description>&lt;p&gt;Biologically Feasible Deep Learning (new paper)&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/1610.00161/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>q-bio.NC</category></item></channel></rss>
