Yue Liu

This paper trains a recurrent neural network to generate grammatical action sequences and identifies experimentally testable predictions about its internal representation and generalization.

The paper investigates how primate frontal cortex represents action grammar structure during novel problem-solving tasks and identifies preSMA as a key region for encoding grammatical properties.

This paper develops a neural network model of task switching, showing how rule representation and error monitoring emerge through training and how inhibitory neurons control subspaces of activity.

This study shows that hippocampal neurons in mice exhibit consistent firing sequences across seconds and minutes during experimental sessions.

This study shows DISC1-NTM mice have impaired risk avoidance, linked to reduced activity in nucleus accumbens parvalbumin interneurons.

This paper identifies constraints for linear recurrent neural networks to generate scale-invariant sequential activity through eigendecomposition analysis.

This study characterizes population coding in monkey prefrontal cortex and hippocampus during learning using a new metric called normalized distance.

This paper introduces a biologically detailed neural circuit model that generates scale-invariant time cells through a combination of calcium-activated currents and inverse Laplace transform network structures.