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Found 16 papers, 5 papers with code
Learning with 3D rotations, a hitchhiker's guide to SO(3)
Many settings in machine learning require the selection of a rotation representation.
Network bottlenecks and task structure control the evolution of interpretable learning rules in a foraging agent
Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems.
Revising clustering and small-worldness in brain networks
As more connectome data become available, the question of how to best analyse the structure of biological neural networks becomes increasingly pertinent.
Emergent mechanisms for long timescales depend on training curriculum and affect performance in memory tasks
We find that for both tasks RNNs develop longer timescales with increasing $N$, but depending on the learning objective, they use different mechanisms.
Available observation time regulates optimal balance between sensitivity and confidence
Tasks that require information about the world imply a trade-off between the time spent on observation and the variance of the response.
The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks
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.
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Locally adaptive cellular automata for goal-oriented self-organization
In this paper, we propose a new model class of adaptive cellular automata that allows for the generation of scalable and expressive models.
When to be critical? Performance and evolvability in different regimes of neural Ising agents
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.
Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents
The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem.
Topology-dependent coalescence controls scaling exponents in finite networks
Here we show that distinct empirical exponents arise in networks with different topology and depend on the network size.
Tackling the subsampling problem to infer collective properties from limited data
However, despite the development of large-scale data-acquisition techniques, experimental observations are often limited to a tiny fraction of the system.
Spatial and temporal correlations in neural networks with structured connectivity
We show that the network dynamics and connectivity jointly define the spatiotemporal profile of neural correlations.
Assessing aesthetics of generated abstract images using correlation structure
We demonstrate that even with the randomly selected weights the correlation functions remain largely determined by the network architecture.
The dynamical regime and its importance for evolvability, task performance and generalization
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.
Tailored ensembles of neural networks optimize sensitivity to stimulus statistics
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
Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence
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
