Science is really a team effort. The people below are collaborating with me on different Projects.
I am always looking for new people to join my lab. For current open internship projects, see this page. Since we do mostly modelling, computer simulations and advanced data analysis (that means, bluntly said, computer programming and mathematics), some affinity and experience with math and programming would really help (see here for an overview). If you’re motivated and willing to learn, you don’t have to be an expert of course, but especially for short internships it is better to be able to focus on the science than on learning how to program.
If I do not have open PhD/postdoc vacancies, these are good resources to start looking for jobs in Computational Neuroscience!
|Lauren Keizer is modelling microcircuits of barrel cortex.|
|Angeliki Sideri measured the information transfer of inhibitory-excitatory microcircuits.|
|Anna Chita is modelling the effects of deprivation on barrel cortical circuits|
|Camille de Keijser is analyzing in vitro single cell data|
|Joris Bottelier is is using ‘FORCE’ learning to study recurrent neural networks that can learn to perform tasks.|
|Ildefonso Ferreira Pica is modelling the effect of dopamine on the balanced state in networks of barrel cortex.|
|Lino Vliex is integrating biologically realistic models of barrel and motor cortex, in order to evaluate the quality of whisker-related information processing. This to answer the question to what extend motor and sensory information are optimally integrated in (active) sensing tasks.|
|Thijs van Loo is using ‘FORCE’ learning to study recurrent neural networks that can learn to perform tasks.|
|Nishant Joshi is a SmartNets student, modelling the effect of single-neuron non-linearities on network behaviour.|
Tea Tompos is implementing biophysical principles of intracellular information processing in neuromorphic software and later hardware.
Filip Novický is modelling the effects of serotonin and its relation to attention.
Mark Bensman modelled microcircuits of barrel cortex.
Saskia Okkerman investigated how the method to measure information transfer depends on the number of excitatory and inhibitory neurons
Pedro Alonso Gonzáles investigated the differences between threshold adaptation and spike frequency adaptation on information transfer.
Lois de Groot used ‘FORCE’ learning to make recurrent neural networks that can learn to perform tasks.
Joost van Tiel analyzed the data Linda Wouter modelled in the plasticity after whisker deprivation in barrel cortex project.
Vaishnavi V used‘FORCE’ learning to make recurrent neural networks that can learn to perform tasks.
Additaya Sharma is modelled the balanced states in networks of barrel cortex.
Hendrik Scheeres is used ‘FORCE’ learning to make recurrent neural networks that can learn to perform tasks.
Xenia Sterl was modelling the effect of dopamine on microcircuits of barrel cortex.
Sam Verhezen measured the information transfer of inhibitory-excitatory microcircuits.
Dimitris Chatzakis compared different methods to measure information transfer, and how that depends on neuron type during his Erasmus internship.
Eva Koenders modelled the VC data Ate Bijlsma recorded, in order to investigate the threshold behaviour in barrel cortical neurons.
Charlee Fletterman has compared models of predictive coding.
Isolde Kuijper has investigated the effects of adaptation and non-linear processing on optimal coding in single neurons.
Jiri Brummer has investigated biologically realistic balanced networks.
Pantelis Leptourgos has researched Bayesian detection in single neurons.
Stefan Bucher has fitted GLM models to hippocampal in vitro data.
Laura van Heerden has modelled the high-conductance state in thalamocortical relay neurons.
Arjan Groen has calculated the reliability of spike trains in models of CA3 of hippocampus.
Lisanne Huurdeman has analysed in-vitro data of thalamocortical relay cells.