06. Dezember 2021, 16:30-18:00, Raum 2030, Cognium, Hochschulring 18

Voltage imaging with genetically encoded voltage indicators: Development and application

Prof. Dr. Daan Brinks

Technologies that allow high-speed imaging of cellular dynamics are central to our ability to ask and answer new questions in cell biology and neuroscience. Here, I will focus on voltage imaging: the optical recording of membrane potentials and their fast dynamics in excitable cells. I will touch upon the development of pooled high throughput screens, high-speed microscopes, targeted gene expression schemes and improved near-infrared voltage indicators, which enabled simultaneous in vivo recordings of supra- and subthreshold voltage dynamics in multiple neurons in the hippocampus of behaving mice. I will discuss recent developments in our lab expanding the palette of available tools and applications for voltage imaging in vitro and in vivo, and touch upon recent functional transcriptomics work that enhances the potential of voltage imaging as a diagnostic tool.


17. Januar 2022, 16:30-18:00, Zoom

Computation spike by spike - hardware and wetware

Prof. Dr. Alberto Garcia-Ortiz and Prof. Dr. Klaus Pawelzik

Recent advances in machine learning with deep neural networks (DNNs) show impressive performances in solving difficult problems. However, current DNN approaches are still inefficient when compared with their biological counterparts. It appears that evolution has found solutions that are still superior to the current technical implementations. Spiking neural networks (SNN) could offer an alternative to standard CNNs. Like the brain, SNN can operate reliably using mechanisms that are inherently non-reliable. Beside robustness, SNN have further advantages like the possibility of higher energy efficiency and more efficient asynchronous parallelization.

However, current implementations of SNNs require hundreds of cores with large and complex circuits. We present an alternative approach, the ‘Spike-by-Spike’ (SbS) networks, which represent a compromise between computational requirements and biological realism that preserves essential advantages of biological networks while allowing a much more compact technical implementation. To fully exploit the robustness and efficiency of SbS, dedicated hardware architectures are required. By combining optimized hardware architectures with stochastic and approximate processing approaches, this new approach aims to improve the performance of neural networks and their energy consumption by at least one order of magnitude.


24. Januar 2022, 16:30-18:00, Zoom

Decoding higher order cognition from invasive brain signals

Prof. Dr. Christian Herff

The decoding of higher order cognition directly from recordings of neural activity in the brain could enable a new generation of prosthetic devices. Accurate information about memory processes, reward perception and attempted speech and motor activity will allow targeted interventions and next-level human-computer interaction. In this presentation, I will present work with neurological patients that have electrodes implanted deep into their brains for clinical procedures. By piggybacking on these clinical routines, we are able to record high-fidelity neural activity across a variety of brain areas and align them to cognitive tasks. Through the application of machine learning, we are able to decode higher-order cognition from these recordings and process the output in real-time.


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