Brent Doiron

This paper introduces Rotational Component Analysis (RCA) to detect low-dimensional rotational dynamics in neural data and shows these dynamics constrain cue-evoked behavior and learning across species.

This paper uses operator-valued free probability to analyze how global and local circuit structures affect population activity dimensionality in recurrent neural networks with heterogeneous gains.

This study proposes a synaptic plasticity model explaining the formation of micro-clusters in mouse V1 through thalamo-cortical connectivity and recurrent circuit interactions.

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.

This study shows that combining Hebbian plasticity with homeostatic inhibition allows for stable, tunable neuronal assemblies rather than binary outcomes.

The paper shows that mouse primary visual cortex integrates non-visual signals from top-down and stimulus-gated pathways during grating stimulation.

The paper introduces FACED 2.0, a high-speed imaging technique that enables simultaneous voltage and calcium activity recording in vivo across large neuronal populations.

This study shows that causal spike-timing-dependent plasticity (STDP) prevents neuronal assemblies from merging when they share neurons, maintaining their distinct identities.

This study develops models of cortical networks that incorporate short-term synaptic depression to explain both asynchronous and synchronous activity observed in vitro.

The paper shows that shared variability in sensory neurons aligns with task-relevant features and action plans, and that this alignment enhances behaviorally relevant information.

The paper investigates spatial organization of orientation tuning in mouse visual cortex and identifies micro-clustered organization of orientation preference.

This study investigates how synaptic unreliability affects noise correlations in balanced neuronal networks, finding opposing effects of feedforward and recurrent synaptic reliability.

The paper investigates how shared dynamics in neuronal activity between areas arise from local interactions or external inputs using spiking neuron models.

This paper introduces SNOPS, a method to automatically customize spiking network models to match large-scale neuronal recordings, revealing limitations of current models.

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 correlated variability in segregated cortical networks and finds that excitatory-inhibitory functional assemblies explain enhanced correlations.

The paper identifies distinct memory storage in the direct and indirect basal ganglia pathways, showing normal motor memory is preserved in the direct pathway while aberrant inhibitory memory is stored in the indirect pathway.

The paper investigates how recurrent circuits in the primary visual cortex influence visual perception by analyzing the spatial arrangement and feature preferences of neuronal ensembles.

The paper shows that recurrent neural circuits with Poisson spiking variability perform optimal Bayesian inference by sampling from posterior stimulus distributions.

The paper shows VIP neurons scale local gamma oscillation gain and suppress global gamma coherence based on stimulus properties and retinal image statistics.

The paper shows that subpopulation codes can modulate information across cortical states, with inhibitory circuitry playing a key role in this process.

The paper investigates how inhibitory interneurons contribute to auditory cortex plasticity after peripheral damage, revealing cell-type-specific roles in restoring sound processing.

This study shows that network connectivity determines population code performance by influencing stimulus tuning and internally generated fluctuations in spiking neuron models.

The paper shows that perceptual performance and correlated variability in sensory cortex are inversely related due to observers using a general decoding strategy rather than optimal stimulus-specific decoding.

The paper investigates how different inhibitory STDP rules in parvalbumin and somatostatin neurons affect assembly formation and stability in the mouse orbitofrontal cortex.

The paper shows that recurrent neural circuits can encode stimulus features through population codes, with variability aligned to stimulus directions and modulated by internal priors.

This paper develops a model of coupled neural circuits that perform distributed Bayesian inference in high-dimensional stimulus spaces using Langevin sampling.

The paper shows how SOM-mediated modulation in E-PV-SOM networks can increase neuronal gain and stability simultaneously, depending on connectivity and network state.

The paper uses single neuron models to explain how Temporal Interference stimulation works and identifies conditions under which neurons fire in response to specific high-frequency inputs.

The paper shows that model cortical networks with physiologically accurate inhibitory coupling generate low-dimensional shared variability, matching in vivo recordings and explaining attentional modulation of variability between brain areas.