Peter E. Latham

This paper introduces a neural model that uses low-dimensional representations of module reuse to accelerate learning, showing consistency with primate behavioral and neural data.

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

The paper develops an efficient computing theory to explain optimal representations in prefrontal cortex working memory tasks, showing compositional and contextual codes as extremes of a spectrum determined by task structure.

Mice infer stimulus-side priors from recent choices; prior signals are decodable pre-stimulus in 24.0% to 30.2% of 242 brain regions, including early visual and motor areas.

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

The paper proposes a normative plasticity rule that combines fast and slow synaptic plasticity to enable concurrent control and learning, improving task performance and robustness.

This paper develops a theory explaining BCI learning through low-dimensional control strategies, validated with experimental data.

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

This study uses a circuit model to investigate how neural activity evolves during abstract knowledge acquisition and how it relates to experimental observations.

The paper predicts neural circuit structure across species by optimizing a three-layer neural network to achieve efficient learning, recovering mammalian 3/2 and invertebrate 7/2 scaling laws.

The paper proposes a method for MAP inference in linear models using sparse connectivity, inspired by sister mitral cells in the olfactory system.

This paper introduces DGNs, a biologically plausible learning rule that outperforms backpropagation in efficiency and forgetting resistance, inspired by cerebellar structure and function.

This study formulates olfactory learning as a Bayesian optimization process and shows a computational model can identify odors from few observations and learn reward associations with a plausible neural architecture.

The paper shows excitatory and inhibitory neurons in mice are equally selective during decision-making and develop similar selectivity changes during learning.

The paper develops a method to detect differential correlations in neural data and applies it to V1 recordings, finding that V1 contains information consistent with behavioral performance.

The paper investigates individual differences in confidence reporting by analyzing how people estimate confidence under uncertainty, finding that some individuals use perceived probability while others use Fisher information.