David Hansel

Synchrony dynamics underlie irregular neocortical spiking

The paper shows that cortical spiking variability arises from weak synchrony rather than intrinsic properties, with synchrony dynamics shifting in response to sensory input.

Reward-driven adaptation of movements requires strong recurrent basal ganglia-cortical loops

This paper presents a theoretical model of the BG-thalamo-cortical network to explain how reward-driven movement adaptation occurs through closed-loop dynamics and dopamine-dependent plasticity.

The fluctuation-based regime of thalamocortical circuitry

The paper shows that fluctuating thalamic input, not average input, can drive cortical spiking in mouse V1.

Idiosyncratic choice bias and feedback-induced bias differ in their long-term dynamics

The paper compares the long-term dynamics of idiosyncratic choice bias and feedback-induced bias, finding that feedback-induced bias is transient while idiosyncratic bias remains stable.

Mechanisms underlying the response of mouse cortical networks to optogenetic manipulation

The paper investigates how mouse cortical networks respond to optogenetic stimulation of PV neurons, finding paradoxical activity changes in layer 5 and proposing network architectures that explain these responses.

Idiosyncratic choice bias in decision tasks naturally emerges from intrinsic stochasticity in neuronal network dynamics

The paper quantifies idiosyncratic choice biases in decision tasks and shows they emerge from neuronal network dynamics, not experimental context.

Spatiotemporal limits of optogenetic manipulations in cortical circuits

The paper benchmarks optogenetic silencers in mice to assess their spatial and temporal effects on cortical circuits, finding that photoinhibition extends beyond light spread and can produce paradoxical effects.

Neuronal activity and learning in local cortical networks are modulated by the action-perception state

The paper shows that neuronal patterns in motor cortex change during brain-machine interface learning to align with the current action-perception state, with pre-learning activity predicting these changes.

Emergent orientation selectivity from random networks in mouse visual cortex

This study shows that random connectivity in mouse V1 can produce orientation selectivity matching experimental data.

How strong are correlations in strongly recurrent neuronal networks?

This study develops a theory for strong correlations in recurrent neuronal networks and shows how feedforward structure influences their spatial and scaling properties.

A Canonical Neural Mechanism for Behavioral Variability

This study demonstrates that a model of recurrent neural networks can generate irregular motor behaviors and matches behavioral variability statistics in humans and songbirds.

Comment on “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons”

The paper comments on a study that identifies two types of asynchronous activity in spiking neuron networks and discusses the implications for slow neural dynamics.