Kenneth Kay

The paper shows that path-invariant activity in V1 neurons is modulated by visual stimuli rather than replaced, linking V1 to a brain-wide network involved in learning.

This paper reframes transitive inference as a linear classification problem using least-squares estimation, showing it can reproduce key empirical features of TI without explicit assumptions about transitivity.

The paper develops a task for rats to study self-correction and finds distinct hippocampal representations during this process.

This paper shows that recurrent neural networks can learn and represent causal structures from observational data, forming implicit beliefs about the full graphical structure even when trained only on pairwise queries.

This paper develops an artificial neural network model with synaptic plasticity and neuromodulation to study relational learning and fast knowledge reassembly through transitive inference.

The paper develops a mathematical framework to explain how learning models, including neural networks, can perform transitive inference through statistical principles and representation learning.

This paper develops and tests neural networks that perform transitive inference, showing they generalize and exhibit behaviors similar to humans, while also revealing inconsistencies between human behavior and certain network solutions.

The paper shows hippocampal neurons cycle between representations of alternative future locations at 8 Hz, driven by theta rhythm, and also represent heading direction alternatives.