Vahid Rostami

The paper develops a deep-learning framework to learn interpretable representations of whole-brain neural dynamics in Drosophila without neuronal identification, revealing factorized organization along metabolic state, sensory modality, and stimulus valence axes.

The paper shows that target information modulates neural and behavioral variability in motor cortex through a spiking attractor model, explaining reaction time and variability patterns in monkeys.

This paper develops a cortical model with excitatory-inhibitory clusters that captures spiking statistics, inter-area propagation, and variability dynamics in primate visual cortex.

The paper compares two SNN simulation approaches, NEST and GeNN, showing GeNN's efficiency for large-scale and real-time simulations on GPUs.

The paper compares two approaches for applying maximum-entropy methods to neuronal activity samples and discusses their implications for sampling in neuroscience.