Bruno B. Averbeck

The paper identifies a neural signal across limbic areas that predicts motivational disengagement independent of reward expectations, suggesting collective computation underlies motivation.

This study shows that value coding in the primate cortico-basal ganglia system varies across motor responses, challenging the idea of motor-system-invariant value signals.

This study shows that long-term learning shifts information processing from the ventral striatum to the orbitofrontal cortex in macaques, challenging traditional theories of reinforcement learning.

This study compares the retrograde transduction efficiency of four viral vectors in dopaminergic neurons of rhesus monkeys after striatal injection.

The paper identifies a thalamostriatal circuit mechanism by which internally constructed outcomes, like safety, acquire motivational value through synaptic plasticity in mice.

This study develops computational models showing that adolescent brain changes like pruning and myelination create strong attractors for emotionally salient memories, potentially contributing to psychiatric disorders.

This study investigates the amygdala's role in dynamic model arbitration under uncertainty by comparing behavior in monkeys with and without amygdala lesions.

The paper shows that eGlyRs in hippocampal interneurons regulate network activity through developmental and evolutionary conserved mechanisms.

The paper investigates how reversal probability is represented in prefrontal cortex neurons and recurrent neural networks during probabilistic reversal learning, finding stable initial states that drive non-stationary dynamics.

The paper identifies a novel inhibitory interneuron subtype in the dentate gyrus that selectively regulates mossy cell function and shows this pattern is evolutionarily conserved across species.

This study investigates the role of somatostatin neurotransmission in prefrontal cortical circuits during learning, showing it shapes configural representations and network dynamics related to salient outcomes.

The paper identifies enhancer-AAV tools for specific neuronal subtypes and validates them across species.

This study investigates how ventral frontostriatal circuitry processes symbolic gains and losses in monkeys, identifying the role of the orbitofrontal cortex in coding outcomes and prediction errors.

This study uses a reinforcement learning model to examine how monkeys' motivation to earn symbolic reinforcers is influenced by task state values.

The paper shows that opioids suppress inhibition in hippocampal PV-INs but not neocortex across species and development, highlighting conserved and divergent roles in neural circuits.

The paper shows that energy landscapes in prefrontal cortex neural activity predict decision consistency, reflecting decision confidence in macaques.

The paper demonstrates fast synaptic-like transmission from striatal cholinergic interneurons to dopaminergic axons, showing axonal integration of synaptic input into spiking.

This study investigates how noradrenergic system perturbations affect reversal learning by using a block-based reversal design, finding that choice accuracy is independent of win-stay behavior and noradrenergic control influences sensitivity to positive feedback.

The paper introduces a convolutional neural network to estimate synaptic connectivity from spike trains without requiring parameter tuning, and applies it to construct neuronal circuit diagrams in monkey cortical areas.

This study investigates neural mechanisms of auditory decision making in monkeys, finding that primary auditory cortex encodes task-relevant information before prefrontal and amygdala regions.

This study develops an RNN model of the dlPFC-dSTR circuit to investigate how learning shapes neural representations and action selection in a sequence learning task.

This study examines how learning affects neural population activity dimensionality and information about choices in the prefrontal cortex using high channel count recordings in monkeys.

This paper introduces a decoding framework that leverages low-dimensional latent variables to improve decoding performance in neural population activity.

The paper compares auditory oddball responses in three brain regions of macaques to better understand the neural basis of the mismatch negativity.

The paper examines cross-frequency coupling in macaque auditory cortex to identify patterns of top-down and bottom-up information processing, finding distinct frequency interactions for each direction.