May We Have Your Attention: Analysis of a Selective Attention Task
Eldan Goldenberg, Jacob R. Garcowski, Randall D. Beer
Abstract
In this paper we present a deeper analysis than has previously been carried out of a selective attention problem, and the evolution of continuous-time recurrent neural networks to solve it. We show that the task has a rich structure, and agents must solve a variety of subproblems to perform well. We consider the relationship between the complexity of an agent and the ease with which it can evolve behavior that generalizes well across subproblems, and demonstrate a shaping protocol that improves generalization.
Create a lesson
Related papers
ANTShapes Benchmarking Datasets for Event-Based Neuromorphic Object Classification
M. Middleton, H. Kayan, B. Sen Bhattacharya et al.
Bug Localization from Bug Reports: A Multi-Objective Approach
Waleed Ahmad, Mehtab Kiran Suddle, Maryam Bashir
Synthesis of Hopfield Neural Network: Novel Results
Garimella Rama Murthy
Homo-RAG: Homology-Guided Retrieval-Augmented Generation for Cross-Species Gene Function Prediction
Azrin Sultana
Learning Whom to Trust : Decision-Generated Credibility in Social Learning
Gabriel Bontemps, Abhishek Banerjee
On Scaling Coordinate-Based Neuroevolution: The Quadtree Bottleneck in ES-HyperNEAT
Romain Claret, Michael O'Neill, Paul Cotofrei et al.