Activity patterns

Neuroscientists believe that perception, action and cognition arise from brain’s activity. A major challenge in neuroscience is to understand how brain’s complex circuits give rise to activity patterns that support these different functions. I will discuss different ways of mapping neural circuits in the brain, and how we can relate the structure of neural circuits to the computations that take place within them, with an emphasis on the visual system.

Activity patterns of neural populations in natural and artificial neural networks constitute representations of data. The nature of these representations and how they are learned are key questions in neuroscience and deep learning. In his talk, I will describe my group's efforts in building a theory of representations as feature maps leading to sample efficient function approximation. Kernel methods are at the heart of these developments. I will present applications to deep learning and neuronal data.

Both computational and experimental results in single neurons and small networks demonstrate that very similar network function can result from quite disparate sets of neuronal and network parameters. Using the crustacean stomatogastric nervous system, we study the influence of these differences in underlying structure on differential resilience of individuals to a variety of environmental perturbations, including changes in temperature, pH, potassium concentration and neuromodulation. We show that neurons with many different kinds of ion channels can smoothly move through different mechanisms