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Filip Vercruysse

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Room: MAR 5.016
Tel.: +49 30 314 24391
filipvercruysse@gmail.com

Nothing in cortical computation makes sense except in light of predicting future outcomes of behavioural decisions

Filip Vercruysse studied Chemical Engineering at Ghent University (Belgium) before obtaining a PhD in Neuroscience from the École Polytechnique de Lausanne (EPFL, Switzerland). He has a background in experimental and computational neuroscience and is deeply committed to understand the role of feedback processes, i.e. expectation, contextual modulation, on sensory representation. More specifically, he uses computational and theoretical methods to understand the consequences of feedback connections on local network dynamics and how this facilitates information processing in primary sensory systems. The key circuit elements of his spiking neural networks include plastic synapses, dendritic processes and different interneuron subtypes. This allows him to investigate the self-organising properties of biologically inspired microcircuits and their relation with behavioural outcomes.

Filip's Research

Burst Control in Cortical Circuits

The existence of specialized mechanisms for burst generation in pyramidal cells (PCs) suggests that bursts are likely to be an important temporal feature of neural signals. In L5 PCs bursts occur at a low, but consistent rate, and are thought to arise from active dendritic processes. Given that burst activity relies on dendritic threshold mechanisms, it appears likely that low burst activity require homeostatic control, but the underlying mechanisms are not resolved. In this research project we model a biologically inspired circuit diagram of a self-organized microcircuit with different inhibitory cell types and plasticity rules to control the burst and population rate of PCs. Our work shows that inhibitory plasticity rules may serve as building blocks to self-organise complex network architectures and allows us to investigate coding properties of bursting units without the need for tuning of input or noise levels.