Neural coding: How does the brain use prediction and timing to encode information?
We are interested in four main topics:
- The formation of predictions, both through space and time, and the role of predictions in unsupervised learning.
- The nature of the neural code. How do neuronal networks efficiently encode information, given the great amount of redundancy across space and time? What are the codewords composing the neural dictionary? Neurons encode information with all-or-nothing electric pulses lasting about 1 ms. Does the number of pulses give information, or the timing of these pulses? How do populations of neurons encode information on short time-scales? What is the geometry of those manifolds? My hypothesis is that the principal means of information encoding is carried, as an analogue signal, by the relative timing between spikes in large populations of neurons.
- Specific contributions of distinct classes of excitatory and inhibitory neurons. In particular, I am trying to understand the different roles of PV+ and SOM+ interneurons in regulating the activity of pyramidal neurons according to predictions. Furthermore, I try to understand how bursting and non-bursting excitatory neurons encode information differently.
- The way in which the state of the organism (wakefulness, sleep, attention) affects sensory processing and cortical communication.
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
Five Key Publications
Onorato I, Neuenschwander S, Hoy J, Lima B, Rocha K-S, Broggini AC, Uran C, Spyropoulos G, Womelsdorf T, and Fries P, Niell C, Singer W, Vinck M. 2019. A distinct class of bursting neurons with strong gamma synchronization and stimulus selectivity in monkey V1. Preprint available on bioRxiv.
Peter A, Uran C, Klon-Lipok J, Roese R, van Stijn S, Barnes W, Dowdall JR, Singer W, Fries P, Vinck M. 2019. Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations. Elife.
Grossberger L, Battaglia FP, Vinck M. 2018. Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. PLoS Computational Biology.
Vinck M, Brito-Batista R, Knoblich U, Cardin JA. 2015. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding. Neuron.
Vinck M, Womelsdorf T, Buffalo E, Desimone R, Fries P. 2013. Attentional modulation of cell- class-specific gamma-band synchronization in awake monkey area V4. Neuron.