The Neurophysiology & Computational Neuroscience Lab, directed by Dr. Arij Daou, studies how the brain generates, learns, modifies, and preserves precise sequences over time. Sequential structure is central to brain function: it appears in speech, birdsong, skilled movement, memory, navigation, decision-making, and learned behavior. Yet we still do not fully understand how neural circuits produce reliable temporal patterns while remaining flexible enough to learn, adapt, and recover from perturbation.
A major focus of the lab is the neural basis of vocal learning and sequence generation in the zebra finch song system. Like human speech, birdsong is learned through auditory experience, practice, feedback, and gradual refinement. This makes the zebra finch a powerful model for studying how the brain learns, stores, and executes precise motor sequences. The lab is particularly interested in HVC, a premotor cortical analogue involved in song timing, where distinct neuronal populations fire with remarkable temporal precision during singing. By studying the cellular and circuit mechanisms of HVC, the lab aims to uncover how local microcircuits generate the timing structure required for learned vocal behavior.
The lab also investigates broader mechanisms of learning, memory, and circuit dysfunction using rodent models. These studies examine hippocampal physiology, spatial learning, neuroinflammation, disease-related changes in excitability, and the relationship between cellular mechanisms and behavior. Across model systems, the lab studies both synaptic plasticity and intrinsic plasticity, with a particular interest in how ion channels, inhibition, neuromodulation, and network architecture shape neuronal excitability and temporal coding.
Experimentally, the Daou Lab integrates whole-cell patch-clamp electrophysiology, in vivo recordings, neuropharmacology, anatomical and neurosurgical approaches, behavioral analysis, immunohistochemistry, and circuit identification strategies. These approaches allow the lab to link single-neuron properties to circuit dynamics and behavior, from membrane excitability and firing phenotypes to sensory processing, vocal learning, memory, and disease-relevant dysfunction.
Computational neuroscience is central to the lab's research program. The lab develops and applies biophysically realistic neural circuit models, single-neuron and network simulations, optimization-based inference of circuit connectivity, dynamical systems analysis, machine learning, and explainable AI methods for neural, behavioral, acoustic, imaging, and biomedical data. This combined experimental-computational strategy allows the lab to move beyond descriptive neuroscience toward mechanistic models of how circuits compute, learn, and adapt.
The long-term vision of the Neurophysiology & Computational Neuroscience Lab is to uncover general principles by which biological neural circuits generate structured behavior. By combining rare experimental capabilities with rigorous computational analysis, the lab aims to understand how the brain learns sequences, maintains timing precision, adapts to change, and fails in disease. The lab is also committed to training students at the interface of neuroscience, engineering, computation, and medicine.
For more information, please visit the lab group website
Contact:
Arij Daou