The Artificial Cognitive Systems lab @DondersInst headed by @marcelge investigates the computational principles that govern natural and artificial intelligence.

Nijmegen, Nederland
Joined April 2016
New preprint out! Probabilistic Forecasting via Autoregressive Flow Matching. arxiv.org/html/2503.10375v1
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Our new preprint "PAM: Predictive attention mechanism for neural decoding of visual perception" introduces a novel approach that learns output queries. Beneficial if queries are n/a, as in neural decoding! w/ @lynnle_ai @artcogsys @yagmurgucluturk @umuguc biorxiv.org/content/10.1101/…
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Discovering dynamic symbolic policies with genetic programming. The evolved policies: -consist of interpretable dynamics equations -offer transparency in the latent state -robust under partial observability -generalize to different task settings arxiv.org/abs/2406.02765
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I'm excited to share our new preprint 'Subspace Node Pruning'! We present a novel perspective on pruning computational units from pre-trained deep networks with minimal performance loss. (0/10)
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Train RNNs efficiently without gradients! Our learning algorithms (ANP & DANP) offer a robust solution by - Requiring only forward passes - Avoiding vanishing gradients - Matching BP (BPTT) performance - Enabling neuromorphic hardware compatibility arxiv.org/pdf/2405.08967v1
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[1/8] Our paper "Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain" is published! @PLOSCompBiol #brainreading 🧠📖 Full paper: doi.org/10.1371/journal.pcbi…
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As AI adoption becomes more widespread, so do concerns about its energy consumption. Our latest paper proposes a method to train AI models more efficiently. The method reduces energy use by more than half when training a ResNet-18 model on ImageNet. arxiv.org/pdf/2405.02385
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Check our paper to see how a simple bistable model can capture the essence of epileptic dynamics. Networks of such bistable models can be even more powerful in interpreting real data. Joint work with @A_ElGazzarr @DaniSBassett @Fabiopas82 @marcelge
A new publication from DBI2 is available🥳! "Analytical Characterization of Epileptic Dynamics in a Bistable System" arxiv.org/abs/2404.03409
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Excited to share our perspective paper on Universal differential equations for neuroscience. arxiv.org/abs/2403.14510. In this manuscript, we argue for UDEs as a unifying framework to integrate mathematical modeling with machine learning in neuroscience.
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Come join us! Postdoctoral research position in efficient machine learning as part of the DBI2 research consortium: ru.nl/node/67534
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We are very happy to announce that the DBI2 website is finally live!🥳 dbi2.nl/. Please visit our website to find out about our team and research activities!
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New paper out! Advancements in spiking neural network communication and synchronization techniques for event-driven neuromorphic systems sciencedirect.com/science/ar…
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Unlocking speech for paralyzed individuals using Brain-Computer Interface Technology and AI ru.nl/donders/news/news/@138… via @Radboud_Uni
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Beyond excited: our manuscript on PhenoScore is out in @NatureGenet! By combining facial recognition technology with phenotypic data analyses, we recognize 37/40 investigated syndromes and even establish new phenotypic subgroups for #ADNP! tinyurl.com/yaent4yw
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I'm happy to share Gabriel's (@gaboraya) post on symmetry breaking in diffusion models! Link: gabrielraya.com/blog/2023/sy… Spontaneous symmetry breaking is behind the standard model of particle physics... it turns out it is also behind the generative powers of diffusion models!
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Our "The neuroconnectionist research programme" paper has been published in Nature Reviews Neuroscience! rdcu.be/ddkIe
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Come join us! Senior researcher position in reinforcement learning leading towards a tenured assistant professorship: ru.nl/en/working-at/job-oppo…
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