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		<title>Self-organization and optimality in neuronal networks</title><link>https://uni-tuebingen.de/en/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/self-organization-and-optimality-in-neuronal-networks/</link><description>Der RSS Feed der Universität Tübingen</description><language>en-EN</language><copyright>Universität Tübingen</copyright><pubDate>Wed, 11 Mar 2026 14:35:05 +0100</pubDate><lastBuildDate>Wed, 11 Mar 2026 14:35:05 +0100</lastBuildDate><item><guid isPermaLink="false">news-105102</guid><pubDate>Wed, 20 Mar 2024 19:03:00 +0100</pubDate><title>Congratulations, Oleg! First student defended his thesis. </title><link>https://uni-tuebingen.de/en/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/self-organization-and-optimality-in-neuronal-networks/newsfullview-selforganizationneuralnetworks/?tx_news_pi1%5Baction%5D=detail&amp;tx_news_pi1%5Bcontroller%5D=News&amp;tx_news_pi1%5Bnews%5D=105102&amp;cHash=8eb5da32526e860dfb337d289ecfcb62</link><description></description><content:encoded><![CDATA[<p>Very proud of Oleg, who learned so much during his PhD and made so many different contributions (many still to appear)! Happy that our collaboration continues. All the best wishes for the postdoc and next steps!</p>]]></content:encoded><category>SelfOrganizationNeuralNetworks-Aktuell</category></item><item><guid isPermaLink="false">news-105105</guid><pubDate>Tue, 16 Jan 2024 10:40:00 +0100</pubDate><title>Two papers accepted to ICLR 2024</title><link>https://uni-tuebingen.de/en/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/self-organization-and-optimality-in-neuronal-networks/newsfullview-selforganizationneuralnetworks/?tx_news_pi1%5Baction%5D=detail&amp;tx_news_pi1%5Bcontroller%5D=News&amp;tx_news_pi1%5Bnews%5D=105105&amp;cHash=375599307a3d35ebfb0cf24f4f93f71b</link><description></description><content:encoded><![CDATA[<p>Congratulations on Aaron, Sina, Roxana, Tim and Manos, great work! Now we are real ML people!</p><p>1. <a href="https://arxiv.org/abs/2306.16922" target="_blank" class="external-link" rel="noreferrer">The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks</a><br> 2. <a href="https://openreview.net/pdf?id=xwKt6bUkXj" target="_blank" class="external-link" rel="noreferrer">Emergent mechanisms for long timescales depend on training curriculum and affect performance in memory tasks</a></p>]]></content:encoded><category>SelfOrganizationNeuralNetworks-Aktuell</category></item><item><guid isPermaLink="false">news-105099</guid><pubDate>Sun, 09 Apr 2023 11:43:00 +0200</pubDate><title>Roxana&#039;s paper on attention appears in Nature Communications</title><link>https://uni-tuebingen.de/en/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/self-organization-and-optimality-in-neuronal-networks/newsfullview-selforganizationneuralnetworks/?tx_news_pi1%5Baction%5D=detail&amp;tx_news_pi1%5Bcontroller%5D=News&amp;tx_news_pi1%5Bnews%5D=105099&amp;cHash=6863dd180ca6e61f8812b55f75d5c1ff</link><description></description><content:encoded><![CDATA[<p>Very happy that this study is finally out in <a href="https://www.nature.com/articles/s41467-023-37613-7" target="_blank" class="external-link" rel="noreferrer">Nature Communications</a> :</p><p>Abstract:&nbsp;Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons.</p><p>&nbsp;</p>]]></content:encoded><category>SelfOrganizationNeuralNetworks-Aktuell</category></item>
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