Grace W. Lindsay

This paper develops a reinforcement learning model to explain how reward prediction errors modulate attention during value learning in macaques, showing that attentional inversion after negative errors enhances rapid adaptation.

The paper introduces a simplified model of intrinsically bursting neurons that balances biological realism with practical usability for circuit simulations.

This study investigates sequential working memory using MEG data, finding that neural subspaces organize memory during retention but not during encoding, while no sequential replay was observed.

The paper introduces DynVision, a framework for building biologically plausible recurrent convolutional networks to model visual cortex dynamics, showing how different architectures affect temporal phenomena.

The paper adds different types of recurrence to a neural network and finds that bio-inspired recurrence improves classification of noisy images, while task-trained recurrence behaves differently.

This paper shows a circuit model of attention explains both neural and behavioral effects of visual attention across different experimental paradigms.

The paper uses an artificial neural network to study why excitatory and inhibitory neurons differ in selectivity and connectivity, finding that these differences can be explained by a few factors.

The paper investigates how attentional modulation affects performance in a deep convolutional neural network model of the visual system, finding that attention based on higher-level optimization outperforms attention based on neural tuning.

This paper shows that Hebbian learning in a random neural network can replicate mixed selectivity properties observed in prefrontal cortex neurons during complex tasks.