Search Results for author:
Found 7 papers, 1 papers with code
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.
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.
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.
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.
