Flexible computations in the brain
Discovering computational principles that drive information processing in the brain is critical to understanding neural correlates of sensory processing, brain-state and behavior. We investigate the principles of flexible computations in the brain in several neural substrates:
The consequences of excitatory-inhibitory interactions for flexible computations.
We develop biophysical and phenomenological models of excitatory (E) and inhibitory (I) signaling interactions that are constrained by the functional architecture of neural components — from E/I synapses in individual neurons to E-I networks at the mesoscopic scale (hundreds to thousands of neurons) — to unveil principles of flexible information processing in the cortex.
Functional connectivity motifs that underlie flexible computations.
We develop computational methods that will allow structurally and biophysically constrained identification of models of neural systems from dense, high-dimensional neural data. The approach is based on statistical modeling and machine learning techniques.
Consequences of molecular organization of neuromodulatory systems for flexible computations.
We employ particle-based computational modeling techniques to study the consequences of molecular heterogeneity in extracellular space in shaping the spatio-temporal dynamics of volume neurotransmission.