Mark van Rossum Mark van Rossum Edinburgh

Mark van Rossum Background: RUU and UvA (Theoretical Physics Msc and PhD) Univ. Pennsylvania and Brandeis (Computational Neuroscience) Mark van Rossum, Informatics Research interests: plasticity and homoeostasis models computation in networks sensory coding and retinal processing See poster

Mark van Rossum Edinburgh University C omputational N euro S cience meeting Summer 2006 UK’s largest Informatics department (80 staff) Tradition: AI, Linguistics, and Neural nets Strong Neuroscience department

Mark van Rossum What is a Doctoral Training Centre? Students with quantitative background (CS, maths, physics) do PhD at the interface with the Life sciences 7 Doctoral Training Centres in the UK, (EPSRC/MRC funded) DTCs: Imaging, membrane biology, medical devices... Edinburgh: The only Neuroinformatics Centre 10 students/year/DTC (only UK students fully funded) Monitored by International External Board

Mark van Rossum International Context USA: Sloan-Swartz program USA: Human brain project USA: Obligatory data-sharing Germany: Comput. neuroscience initiative (“Das Denken verstehen”) UK: Novel Computation and Cognitive Systems initiative

Mark van Rossum Areas of Neuroinformatics Edinburgh's neuroinformatics Software Systems, Computational Modelling, Neural Engineering Software systems to help understand the brain Data amount is enormous and heterogeneous Concerns about animal expts. require data sharing Need tools to organize and share Need user-friendly tools to simulate

Mark van Rossum Areas of Neuroinformatics Edinburgh's neuroinformatics Software Systems, Computational Modelling, Neural Engineering Understanding the brain in computational terms Models of Parkinson's disease Development of the nervous system Plasticity and learning Cognitive processes and language Applications: basic research, software, machine learning

Mark van Rossum Areas of Neuroinformatics Edinburgh's neuroinformatics Software Systems, Computational Modelling, Neural Engineering Devices linking neuroscience and engineering Neuro-robotics Traditional semi-conductors will reach capacity Use brain-like engineering Better interfaces between hardware and biological tissue: for experiments (silicon patch-clamp) and neuro-prosthesis

Mark van Rossum Areas of Neuroinformatics Software Systems: “brain software” Bioinformatics, databases, simulators Our definition of neuroinformatics: Computational Modelling: “brain as computer” Development, Parkinsons, Plasticity, Cognition Neural Engineering: “hardware and software brain” Neurorobotics, Neuromorphic VLSI, silicon patch- clamp

Mark van Rossum Doctoral Training Centre: 1+3 Provide neuroscience and neuroinformatics training so that students can apply their skills to neuroinformatics A view of many areas to ease choice of PhD project Prepares students for research practice 1 year training + 3 year PhD 1 st year:

Mark van Rossum First Year Taught Training Month 7-12 Neuroscience (existing Masters course) Each week different subject: Molecular, cellular, clinical, expt. methods, imaging, cognitive Remedial teaching Informatics courses (existing MSc courses) Neural networks, neural computation, databases, VLSI Special interest courses Summer projects in experimental labs 1 x 20 weeks, or 2x 10 weeks Prepare for PhD choice, get hands-on experience

Mark van Rossum Years 2-4: PhD Projects Current PhD Projects: ● Prior knowledge for inference ● Neurorobotics ● Head direction cells and place cells in rats ● Modelling and ERP imaging of episodic memory ● Networks for hormone release ● Attentional vision model for video ● Diffusion tensor MRI ● LTP and stability ● Protein networks PhD Projects: ● After 1 st year students identify PhD project ● Two supervisors (typically, Informatics + Biology)

Mark van Rossum Keys to success Teaching shared with existing courses  need few resources Specialized weeks for teaching  the teaching is fun Supervisors compete for students  staff is involved Joint PhD supervision  interdisciplinary research The program is large  collaboration is necessity

Mark van Rossum Situation in the Netherlands Good conditions: Excellent computer science, physics, maths, and engineering Excellent neuroscience Good computation infrastructure Keys to success: Cross-department goodwill Excite the students Face international competition