Integrated Calendar

Sensory perception is shaped by past learning, and is mediated by neuronal circuits in the sensory cortex. However, what are the changes in these neuronal circuits following learning have remained unknown. To reveal the circuit changes, I developed a new associative fear-learning procedure, and using in vivo 2-photon microscopy measured the circuit responses to the associated stimulus following learning. I discovered that associative learning reduces the percentage of neurons responding to the associated stimulus, while the neurons that still respond increase their response strength. These changes are specific to associative learning because non-associative training triggers a very different set of circuit changes. Therefore, associative learning shapes circuit responses in the sensory cortex for more efficient processing of the conditional stimulus, and for higher signal to noise ratio.
The research in my laboratory will continue to address fundamental questions at the levels of cortical neurons, circuits, and behavior. Specifically, how cortical circuits store new information, what are the cortical pathologies in mouse models of autism, and - in the long-term - what are the mechanisms of learning flexible behavior.

I will quickly review the problems quantum mechanics encounters when describing measurement situations (more generally, the quantum-to-classical transition). I will focus on one such solution: models of spontaneous wave function collapse. I will describe their general features. I will discuss the lower and upper bounds on their parameters. I will review their status as phenomenological modifications of quantum mechanics, whose predictions can be tested experimentally.