The research in my group is aimed at understanding the principles of synaptic connectivity in the cerebral cortex. Most of my research projects are linked by a common theme which can be summarized as: inferring synaptic connectivity through the quantitative analysis of neuron morphology. The topics of interest range from the theoretical and computational analyses of real and artificial neural networks and their memory storage capacity, to building cortical connectivity diagrams based on the experimental datasets of neurons reconstructed in 3D, to developing algorithms for automated tracing of neural circuits from light microscopy stacks of images.
Publications
- Sparse learning enabled by constraints on connectivity and function
- Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits
- Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images
- Active learning of neuron morphology for accurate automated tracing of neurites
- Efficient associative memory storage in cortical circuits of inhibitory and excitatory neurons
- Cooperative synapse formation in the neocortex