Ran Darshan

The paper introduces a mechanistic model to infer control parameters of neural circuits from single-trial activity, showing how internal states shape dynamics across trials.

The paper shows that neural activity in non-task-coding dimensions can drive behavior through transient amplification, challenging the assumption that only task-relevant dimensions are behaviorally significant.

The paper evaluates whether the cerebral cortex operates in a fluctuation-driven regime by analyzing spiking and sub-threshold membrane potentials in different cortical areas.

The paper maps functional connectivity in the motor cortex of mice during naturalistic reward-seeking behavior, revealing columnar architecture and hub neurons that influence neighboring cells.

This study identifies synaptic plasticity mechanisms in Drosophila that incorporate reward expectations to drive operant matching behavior.

This paper presents optimized CPU and GPU implementations of the recursive least-squares algorithm for training spiking neural networks, enabling faster training of large-scale models.

The paper shows that task-related neural activity spreads across a cortical network through task-independent synapses, even in untrained neurons.

The paper develops a theory for manifold attractors in trained neural networks, showing how asymmetries and connectivity heterogeneity affect their formation and stability.

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

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.

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

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