15 citations · 57 across the 13 of their papers we have counts for
7 papers · 1 filter
Binding and Perspective Taking as Inference in a Generative Neural Network Model
Mahdi Sadeghi, Fabian Schrodt, Sebastian Otte +1
The ability to flexibly bind features into coherent wholes from different perspectives is a hallmark of cognition and intelligence. Importantly, the binding problem is not only rel…
Active Tuning
Sebastian Otte, Matthias Karlbauer, Martin V. Butz
We introduce Active Tuning, a novel paradigm for optimizing the internal dynamics of recurrent neural networks (RNNs) on the fly. In contrast to the conventional sequence-to-sequen…
Inferring, Predicting, and Denoising Causal Wave Dynamics
Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch +3
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally paramete…
Latent State Inference in a Spatiotemporal Generative Model
Matthias Karlbauer, Tobias Menge, Sebastian Otte +4
Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions. Inferring these factors f…
Fostering Event Compression using Gated Surprise
Dania Humaidan, Sebastian Otte, Martin V. Butz
Our brain receives a dynamically changing stream of sensorimotor data. Yet, we perceive a rather organized world, which we segment into and perceive as events. Computational theori…
Learning Precise Spike Timings with Eligibility Traces
Manuel Traub, Martin V. Butz, R. Harald Baayen +1
Recent research in the field of spiking neural networks (SNNs) has shown that recurrent variants of SNNs, namely long short-term SNNs (LSNNs), can be trained via error gradients ju…