David J. Freedman

The paper identifies the frontal eye field (FEF) as leading a distributed circuit for abstract categorical decisions by analyzing neural activity in monkeys during a visual motion-categorization task.

The paper shows that diverse neuronal firing rates reduce the dimensionality of trial-to-trial covariability in population responses across multiple species.

The paper investigates how neural activity in the primate inferotemporal cortex changes with visual familiarity over multiple timescales, finding that both slow and fast plasticity rules contribute to learning.

The paper addresses the assignment problem in sensory integration by analyzing how redundant representations help disambiguate complex stimuli across modalities and brain regions.

This study investigates learning-to-learn in a neural network model, showing schema formation in a low-dimensional subspace that accelerates learning across similar problems.

The paper shows that long-term training history shapes sensory representations in parietal cortex during decision-making tasks.

This study identifies a novel role of the primate superior colliculus in abstract visual categorization, showing it encodes category information more strongly and quickly than the posterior parietal cortex.

This study identifies a mechanism where hippocampal ensembles are remodeled to support memory-updating in mice during spatial reversal tasks.

This paper shows that rapid multitask learning in reservoir-style networks can occur with sparse, localized plasticity and local error signals, without backpropagation.

This study compares neural activity in the lateral intraparietal area (LIP) during directed and undirected tasks, finding that LIP encodes eye movement prior only in directed tasks.

The paper compares synaptic connectivity in primate and mouse visual cortices, finding primate connections are sparser, and shows that synaptic maintenance costs may explain this difference in artificial neural networks.

This study investigates the roles of prefrontal and parietal cortices in sequential categorical decisions using neural recordings and computational models.

The paper investigates how neuronal encoding and population dynamics in parietal cortex support different categorization tasks, finding that encoding styles vary between tasks and that RNNs support the hypothesis of binary-like encoding through attractor dynamics.

The paper shows that perceptual detection in mice depends on integrating spike counts in V1 rather than instantaneous spiking changes.

The paper investigates category processing in MST and LIP areas during a categorization task, showing MST encodes category and working memory information similar to LIP.

The paper investigates categorical perception in non-human primates using behavioral data and a neural circuit model to demonstrate top-down signaling in the cortex.

The paper shows nonlinear mixed selectivity enables reliable neural computation with lower metabolic energy than pure selectivity.

This study uses RNN models to show that short-term plasticity supports memory maintenance for short delays, while persistent activity emerges for information manipulation tasks.