Jack W. Lindsey

This paper introduces a recurrent network model of the hippocampus that generates barcodes to bind memory elements, reducing interference and enabling flexible retrieval.

The paper shows that striatal dopamine and acetylcholine encode trajectory errors during navigation, with distinct spatial and temporal patterns, and links these signals to learning and behavior.

This study shows that factorization of visual scene information is a common strategy in primate brains and deep neural networks, improving object identity decoding.

The paper resolves a contradiction between striatal reinforcement learning models and SPN synaptic plasticity by showing that iSPN plasticity hinders learning but can be reversed by simultaneous activation of dSPN and iSPN.

This paper proposes a model of recall-gated consolidation that enhances memory storage by prioritizing reliable synaptic changes, showing benefits in noisy environments and explaining behavioral learning phenomena.

This study shows motor cortex is needed for flexible, cue-driven motor sequences but not for automatic sequences, and this dependency changes when sequences are practiced together.

This study investigates how the sensorimotor striatum contributes to automatic and visually-guided motor sequences in rats, finding that it is essential for automatic skills but not sensory-guided sequences.

The paper maps the synaptic connectivity of the Drosophila mushroom body and identifies new circuit components and structures in sensory input processing and output modulation.

Imposing an optic-nerve-like bottleneck in a deep convolutional neural network trained on image recognition causes antagonistic center-surround retinal receptive fields and orientation-tuned primary visual cortex representations to emerge simultaneously.

Optimizing linear and rectifying nonlinear retinal encoding models on natural movies under a fixed firing-rate budget yields ON and OFF midget- and parasol-like ganglion cell types matching primate retina organization.