Sleep is a fundamental biological process observed widely in the animal kingdom, and its disruption has profound impacts on human health. However, the neural circuits generating sleep remain poorly understood. Using techniques developed over the past decade, including optogenetics, cell-type-specific imaging, virus-mediated circuit tracing, and gene expression profiling, we identify key neurons in the sleep control circuits, map their synaptic connections, and identify potential intervention targets for improving sleep.
Lecture title:
“Circuits for Movement”
Movement is the behavioral output of most brain activities. Of movements locomotion is one of the most fundamental common to all animal kingdom. It entails complex neuronal coordination and temporal alteration. This lecture will focus on recent work that has elucidated the functional diversification of locomotor circuits. It will show that spinal locomotor networks are composed of adaptable molecularly-defined circuit modules, and address the role of designated brainstem circuits that serve key functions in gating and context-dependent selection of the motor behavior.
Computer vision – although being still a rather young scientific discipline – in the last decades was able to provide some impressive examples of artificial vision systems that outperform humans. Only recently, a new wave of “deep neural networks” provided solutions that performed superior to other methods on multiple important benchmarks. Strangely enough, these successes were to a lesser degree caused by the development of “new algorithms” but by the increase of computational resources and availability of Big Data. The observation of the potential of deep hierarchical structures for computer vision also caused interest in the human visual system with a deep hierarchical structure with around 8 layers.
In my talk, I will give an overview about today’s knowledge about the primate’s (and by that the human’s) visual system primarily based on neurophysiological research. This part is based on the paper (Krüger et al., 2013) and is in particular addressing computer vision and machine learning scientists as audience. After that, I will give some reflections on recent developments in computer vision in the context of deep learning.
References
N. Krüger, P. Janssen, S. Kalkan, M. Lappe, A. Leonardis, J. Piater, A. J. Rodriguez-Sanchez and L. Wiskott. Deep Hierarchies in the Primate Visual Cortex: What Can We Learn For Computer Vision? I E E E Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1847-1871.
