My lab’s research is divided into three research lines: Circuits-Ensembles-Learning (CEL), funded by ERC and BMF grants and support from the ESI:
Circuits: We investigate how distinct classes of excitatory and inhibitory neurons regulate plasticity and contribute to flexible information processing.
Ensembles: We investigate how ensembles of neurons encode information through spatio-temporal patterns and what the role of spike sequences and bursting are in information encoding and transmission. Furthermore we investigate what the relationship between spontaneous (imagination, dreams) and sensory evoked activity is.
Learning: We investigate how the brain performs self-supervised learning using predictions of the unknown (in space and time), utilizes parallel features for object recognition (motion, form, texture) and uses recurrent networks for object recognition.
To answer these kind of questions, we use a variety of techniques and approaches. We employ machine learning techniques to model predictive relationships among sensory inputs - across space and time. We develop new algorithms for unsupervised clustering of high-dimensional neural datasets. We use information and neural network theory to understand neural coding. We use high-density, multi-areal electrophysiological recordings of neurons, from all cortical layers. This allows us to record many neurons at the same time. We use optogenetics to identify subtypes of neurons - like interneurons or neurons with specific projection patterns - and modify cortical activity. We classify the state of the organism using e.g. pupil diameter. We develop new types of signal processing techniques to deal with electrophysiological data.
See also the personal website
Grossberger L, Battaglia FP, Vinck M (2018). Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. PLoS Comput Biol 14(7), e1006283. https://doi.org/10.1371/journal.pcbi.1006283
Vinck M, Bosman C (2016). More gamma more predictions: Gamma-synchronization as a key mechanism for efficient integration of classical receptive field inputs and surround predictions. Frontiers in Systems Neuroscience. https://doi.org/10.3389/fnsys.2016.00035
Vinck M, Batista-Brito R, Knoblich U, Cardin JA (2015). Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding. Neuron 86(3), 740-754. https://doi.org/10.1016/j.neuron.2015.03.028
Vinck M, Battaglia FP, Womelsdorf T, Pennartz CMA (2012). Improved measures of phase-coupling between spikes and the local field potential. Journal of Computational Neuroscience 33(1), 53-75 https://doi.org/10.1007/s10827-011-0374-4
Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz CMA (2011). An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55(4), 1548-1565. https://doi.org/10.1016/j.neuroimage.2011.01.055