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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1805.10734/</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/1805.10734/feed.xml" rel="self" type="application/rss+xml" /><item><title>A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception — mentioned by mcilai</title><link>https://news.ycombinator.com/item?id=22147533</link><guid isPermaLink="false">urn:hackerpapers:mention:1805.10734:22147533</guid><pubDate>Sat, 25 Jan 2020 17:59:03 GMT</pubDate><description>&lt;p&gt;Deep learning is as close to being a &amp;quot;newtonian theory&amp;quot; of the brain as it gets -- deep learning abstracts away a lot of the complexity of neural systems (e.g., a simple artificial neuron vs a highly complex biological one) while maintaining a number of essential characteristics: massively parallel computation, error tolerance, graceful degradation, distributed representations, information is stored in slowly-changing synapses, and, most importantly: a simple, local, and powerful biologically-plausible-if-you-squint-hard-enough learning rule. The important question to ask is: is the deep…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1805.10734/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>q-bio.NC</category></item></channel></rss>
