Artificial Cognitive Systems

New preprint out! Probabilistic Forecasting via Autoregressive Flow Matching. arxiv.org/html/2503.1037…

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/10.110…

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

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)

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/2405.08967…

[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/journa…

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

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

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.

Come join us! ru.nl/en/working-at/…

Come join us! Postdoctoral research position in efficient machine learning as part of the DBI2 research consortium: ru.nl/node/67534

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!

New paper out! Advancements in spiking neural network communication and synchronization techniques for event-driven neuromorphic systems sciencedirect.com/science/articl…

Unlocking speech for paralyzed individuals using Brain-Computer Interface Technology and AI @1388251/unlocking-speech-paralyzed-individuals-using-brain/" target="_blank" rel="nofollow noopener">ru.nl/donders/news/n… via @Radboud_Uni

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

I'm happy to share Gabriel's (@gaboraya) post on symmetry breaking in diffusion models! Link: gabrielraya.com/blog/2023/symm… Spontaneous symmetry breaking is behind the standard model of particle physics... it turns out it is also behind the generative powers of diffusion models!

Our "The neuroconnectionist research programme" paper has been published in Nature Reviews Neuroscience! rdcu.be/ddkIe

Come join us! Senior researcher position in reinforcement learning leading towards a tenured assistant professorship: ru.nl/en/working-at/…

Come join us! #DeepLearning #MachineLearning #ArtificialIntelligence software engineer: ru.nl/en/working-at/…