Integrated Calendar

The talk is based on Nicolas Bergeron’s book, Section 5.4.

Interhemispheric communication is a comprehensive concept that involves both the synchronization of neural activity as well as the integration of sensory information across the two brain hemispheres. In this work, we explored these properties in the somatosensory system of the mouse brain. We show that during spontaneous activity in awake animals,  robust interhemispheric correlations of both spiking and synaptic activities that are reduced during whisking compared to quiet wakefulness. And that the state-dependent correlations between the hemispheres stem from the state-depended nature of the corpus callosum activity. Further, to understand how sensory information is integrated across the brain's hemispheres, we studied bilateral and ipsilateral responses to passive whisker stimulation using widefield imaging and then employed a virtual tunnel environment to explore bilateral integration in active whisking

We show that a random quasiconformal mapping is close to an affine mapping, while a circle packing of a random Delauney triangulation is close to a conformal map. This is joint work with Vlad Marković.

We give a 2^{tlide{O}(sqrt{n}/epsilon)} -time algorithm for properly learning monotone Boolean functions under the uniform distribution over {0,1}^n. Our algorithm is robust to adversarial label noise and has a running time nearly matching that of the state-of-the-art improper learning algorithm of Bshouty and Tamon (JACM 96) and an information-theoretic lower bound of Blais et al (RANDOM ’15). Prior to this work, no proper learning algorithm with running time smaller than 2^{Omega{n}} was known to exist. The core of our proper learner is a local computation algorithm for sorting binary labels on a poset. Our algorithm is built on a body of work on distributed graph algorithms