Larry F. Abbott

The paper identifies a novel neural code in the mouse medial frontal cortex that supports spatial generalization through extrapolation of coordinates across regions.

The paper shows that coupling neuronal and synaptic dynamics in recurrent networks enables persistent oscillations, a dynamic form of memory without explicit storage or retrieval.

The paper uses connectomics to map a cerebellum-like circuit in weakly electric fish, revealing structured synaptic connectivity that supports predictive sensory processing and continual learning.

The paper investigates the role of cartwheel cells in the dorsal cochlear nucleus, showing they use anti-Hebbian plasticity to enhance inhibition of output neurons during anticipated sounds.

The paper shows mice seek information for its intrinsic value, with distinct neural representations in the orbitofrontal cortex for information and reward.

This study shows that Drosophila use vector-based memory computations in the central complex to navigate odor plumes by tracking their boundaries.

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.

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 how Drosophila neurons use calcium spikes during hyperpolarization to invert two-dimensional vectors in the central complex.

The paper presents a computational model explaining how analog-valued decision information is transferred between neuronal populations through dynamic gating during eye movements.

This study shows that motor cortex activity is high-dimensional and flexible, with distinct neural dynamics for different subskills during a force-tracking task.

The paper develops an end-to-end model of electrosensation in weakly electric fish, using artificial data to train an ANN that extracts object properties.

This paper shows that continuous-attractor dynamics can operate in complex mammalian neural circuits despite apparent heterogeneity in neural responses.

The paper develops a mathematical framework to explain how learning models, including neural networks, can perform transitive inference through statistical principles and representation learning.

This paper develops and tests neural networks that perform transitive inference, showing they generalize and exhibit behaviors similar to humans, while also revealing inconsistencies between human behavior and certain network solutions.

The paper identifies a neuronal circuit in Drosophila that converts allocentric heading and goal angles into an egocentric steering signal through interactions between EPG, FC2, and PFL3 cells.

The paper uses SCAPE microscopy to analyze neural activity in Drosophila brains during different behaviors, revealing both behavior-related and residual high-dimensional activity patterns.

This study identifies hue-selective neurons in Drosophila and shows that recurrent connections are critical for hue selectivity in color vision.

This paper demonstrates that sparse RNNs can perform high-capacity classification through distributed computation and resonance amplification of inputs.

The paper shows how axonal and dendritic spikes interact to enable both learning and transmission of sensory predictions in mormyrid fish electrosensory lobe.

This paper shows that a recurrent neural network can compute the successor representation when synaptic weights match transition probabilities, and demonstrates this with hippocampal data from random foraging.

This study shows that motor units are flexibly controlled by the cortex, challenging the traditional view of common drive and rigid recruitment.

The paper introduces a new model of excitatory-inhibitory networks that generate asynchronous activity without requiring large bias currents, supported by sparse neuron activity and high-variance synaptic weights.

The paper identifies a neuronal circuit in Drosophila that performs vector arithmetic to compute an allocentric traveling-direction signal.

The paper develops a neural network model with anti-Hebbian plasticity to detect familiarity in images, showing better performance than Hebbian models and replicating experimental results.

The paper measures state- and stimulus-dependent filtering properties of inputs to the Drosophila T5 cell and uses a connectomic-constrained model to reconcile motion computation with the circuit's anatomy.

This study shows that artificial neural networks trained on olfactory tasks develop structural and functional features similar to fly and mouse olfactory systems.

This paper develops a computational model showing how thalamic control of cortical dynamics enables flexible motor sequencing through motif-specific inhibition.

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.

This study shows how odor representations shift from piriform cortex to prefrontal cortex, with transient and persistent value-based representations emerging after learning.