Tools to decipher neuronal signalling and computation

点击翻译按钮获取中文摘要

The overall objective of my NSE program is to advance our ability to understand the structural and functional substrates of how information is encoded, in brain circuits, from synapses assemblies to small and long-range networks. These circuits have evolved to solve tasks far beyond the reach of today's most powerful computers while using only the energy budget of a light bulb. Understanding how this is achieved is particularly challenging given the complexity of brain structures and the degree of multidirectional, multiscale interactions within neuronal circuits. Being able to probe the brain across a wide range of scales, from its most intricate components to large neuronal ensembles, in action, is necessary to provide an appropriate appreciation of the computational capabilities of the brain. To undertake this challenge, my NSERC program has focused on developing novel approaches and analytical tools to probe and manipulate live nerve cells and brain circuits across scales. Toward this aim, our main focus is to develop: 1) signal analysis tools to resolve biophysical events (e.g., channel gating and molecular interactions) at the limits of what our experimental tools allow us to address; 2) high-resolution optical microscopy to resolve structural determinants underlying synaptic arrangements in live cell conditions; 3) non-imaging sensing and actuating micro-device to monitor and control neuronal activity in the intact brain, from single cells to large ensembles in freely moving animals to understand signal processing in relevant behavioural context; 4) computer-based modelling to identify how local structural elements determine information coding, from single neurons to neural circuits. We achieve this through collaborative efforts with physicists, mathematicians and engineers. Our research builds on complementary expertise in neuroscience, biophysics, material science, micro-structuring, imaging, signal analysis, circuit integration and miniaturisation to maximise the amount of information collected in the least invasive manners to bridge molecular probing with behavioural assessment. In turn, we integrate knowledge gained experimentally into models to guide further experimental probing to unravel how each component of the system shapes the computational properties of neural networks. Thus, my Discovery Grant has been centered on training research personnel with photonics, mathematics and engineering background aiming to apply their skills to resolve neurobiological questions. While the primary focus is in developing physical and computational tools, the trainees are exposed to experimental components to validate their novel optical, nanotechnological and analytical approaches and to test predictions made by modelling approaches. It also serves to provide trainees with a multidisciplinary know-how that will be critical for their academic or industrial career.