1 code implementation • 17 Apr 2024 • A. René Geist, Jonas Frey, Mikel Zobro, Anna Levina, Georg Martius
Many settings in machine learning require the selection of a rotation representation.
no code implementations • 20 Mar 2024 • Emmanouil Giannakakis, Sina Khajehabdollahi, Anna Levina
Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems.
no code implementations • 28 Jan 2024 • Tanguy Fardet, Emmanouil Giannakakis, Lukas Paulun, Anna Levina
As more connectome data become available, the question of how to best analyse the structure of biological neural networks becomes increasingly pertinent.
no code implementations • 22 Sep 2023 • Sina Khajehabdollahi, Roxana Zeraati, Emmanouil Giannakakis, Tim Jakob Schäfer, Georg Martius, Anna Levina
We find that for both tasks RNNs develop longer timescales with increasing $N$, but depending on the learning objective, they use different mechanisms.
no code implementations • 15 Jul 2023 • Sahel Azizpour, Viola Priesemann, Johannes Zierenberg, Anna Levina
Tasks that require information about the world imply a trade-off between the time spent on observation and the variance of the response.
1 code implementation • 14 Jun 2023 • Aaron Spieler, Nasim Rahaman, Georg Martius, Bernhard Schölkopf, Anna Levina
Biological cortical neurons are remarkably sophisticated computational devices, temporally integrating their vast synaptic input over an intricate dendritic tree, subject to complex, nonlinearly interacting internal biological processes.
Ranked #1 on Time Series on neuronIO
no code implementations • 12 Jun 2023 • Sina Khajehabdollahi, Emmanouil Giannakakis, Victor Buendia, Georg Martius, Anna Levina
In this paper, we propose a new model class of adaptive cellular automata that allows for the generation of scalable and expressive models.
no code implementations • 28 Mar 2023 • Sina Khajehabdollahi, Jan Prosi, Emmanouil Giannakakis, Georg Martius, Anna Levina
To test it we introduce a hard and simple task: for the hard task, agents evolve closer to criticality whereas more subcritical solutions are found for the simple task.
no code implementations • 12 Mar 2023 • Emmanouil Giannakakis, Sina Khajehabdollahi, Anna Levina
The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem.
no code implementations • 11 Nov 2022 • Roxana Zeraati, Victor Buendía, Tatiana A. Engel, Anna Levina
Here we show that distinct empirical exponents arise in networks with different topology and depend on the network size.
no code implementations • 12 Sep 2022 • Anna Levina, Viola Priesemann, Johannes Zierenberg
However, despite the development of large-scale data-acquisition techniques, experimental observations are often limited to a tiny fraction of the system.
no code implementations • 16 Jul 2022 • Yan-Liang Shi, Roxana Zeraati, Anna Levina, Tatiana A. Engel
We show that the network dynamics and connectivity jointly define the spatiotemporal profile of neural correlations.
no code implementations • 18 May 2021 • Sina Khajehabdollahi, Georg Martius, Anna Levina
We demonstrate that even with the randomly selected weights the correlation functions remain largely determined by the network architecture.
2 code implementations • 22 Mar 2021 • Jan Prosi, Sina Khajehabdollahi, Emmanouil Giannakakis, Georg Martius, Anna Levina
Surprisingly, we find that all populations, regardless of their initial regime, evolve to be subcritical in simple tasks and even strongly subcritical populations can reach comparable performance.
1 code implementation • 24 May 2019 • Johannes Zierenberg, Jens Wilting, Viola Priesemann, Anna Levina
Spreading processes are conventionally monitored on a macroscopic level by counting the number of incidences over time.
Neurons and Cognition Disordered Systems and Neural Networks Physics and Society
1 code implementation • 24 May 2019 • Johannes Zierenberg, Jens Wilting, Viola Priesemann, Anna Levina
The dynamic range of stimulus processing in living organisms is much larger than a single neural network can explain.
Disordered Systems and Neural Networks Neurons and Cognition