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	<title>temporal prediction &#8211; BIOENGINEER.ORG</title>
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		<title>One Spiking Neural Network Learns What Happens, When, and How Likely</title>
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		<pubDate>Wed, 07 Oct 2026 14:08:32 +0000</pubDate>
				<category><![CDATA[Biology]]></category>
		<category><![CDATA[brain-inspired AI]]></category>
		<category><![CDATA[Computational neuroscience]]></category>
		<category><![CDATA[Computations Biology]]></category>
		<category><![CDATA[local learning rules]]></category>
		<category><![CDATA[neuromorphic computing]]></category>
		<category><![CDATA[predictive coding]]></category>
		<category><![CDATA[probability learning]]></category>
		<category><![CDATA[recurrent neural networks]]></category>
		<category><![CDATA[spiking neural networks]]></category>
		<category><![CDATA[synaptic plasticity]]></category>
		<category><![CDATA[temporal prediction]]></category>
		<category><![CDATA[University of Tokyo]]></category>
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					<description><![CDATA[Researchers at the University of Tokyo show that a single recurrent spiking neural network can learn event identity, timing, and probability together using local, biologically plausible learning rules and rapidly update its predictions when environmental statistics change.]]></description>
		
		
		
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