5 papers
Modeling bilingual naming performance through laterally-connected SOMs
Divya Prabhakaran, Uli Grasemann, Erie Shivers +3
Computational models have explanatory power to understand cognition in individuals, but there is usually not enough training data to build such models with traditional machine lear…
Attractor Geometry of Transformer Memory: From Conflict Arbitration to Confident Hallucination
Qiyao Liang, Risto Miikkulainen, Ila Fiete
Language models draw on two knowledge sources: facts baked into weights (parametric memory, PM) and information in context (working memory, WM). We study two mechanistically distin…
Evolution With Purpose: Hierarchy-Informed Optimization of Whole-Brain Models
Hormoz Shahrzad, Niharika Gajawelli, Kaitlin Maile +2
Evolutionary search is well suited for large-scale biophysical brain modeling, where many parameters with nonlinear interactions and no tractable gradients need to be optimized. St…
How the Stroop Effect Arises from Optimal Response Times in Laterally Connected Self-Organizing Maps
Divya Prabhakaran, Uli Grasemann, Swathi Kiran +1
The Stroop effect refers to cognitive interference in a color-naming task: When the color and the word do not match, the response is slower and more likely to be incorrect. The Str…
Using context to adapt to sensor drift
J. Warner, A. Devaraj, R. Miikkulainen
Lifelong development allows animals and machines to adapt to changes in the environment as well as in their own systems, such as wear and tear in sensors and actuators. An importan…