Open internship projects

If you would like to do an internship with me, here are a few possible projects. However, do not hesitate to formulate a project yourself!

Network comparison and analysis


Recently, researchers have been able to record high-dimensional neuronal ensembles. However, how to deal with these datasets that contain high-dimensional data but low numbers of trials is still a source of much research. With individual differences between numbers of (recorded) neurons and network structure, it is impossible to obtain many trials. Also, with the recent advances in RNNs and DNNs, the question how different algorithms and network structures implement computational solutions to similar problems becomes relevant. Therefore, it is important to be able to compare networks: what are ‘similar’ networks, and which ones are ‘different’? Can we distinguish networks performing different tasks (for instance layers in cortex) based on their activity?


You will use existing data, either from our department, from online databases or simulated data. You will implement existing comparison methods (see literature) to see to what extent we can group and distinguish network on the basis of these comparison methods. 


Examples of network analysis tools that could be used:

  • Vinck M, Grossberger L, Battaglia F (2018) Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure. Bernstein Conference 2018. doi: 10.12751/nncn.bc2018.0233
  • Williams, A. H., Kim, T. H., Wang, F., Vyas, S., Ryu, S. I., Shenoy, K. V., … Ganguli, S. (2018). Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis. Neuron, 1–17. doi:10.1016/j.neuron.2018.05.015
  • Niru Maheswaranathan, Williams, A., Golub, M., Ganguli, S., & David Sussillo. (2019). Extracting universal algorithmic principles from large populations of recurrent networks. In Cosyne Abstracts 2019, Lisbon, PT. (p. III-51).
  • Hinne, M., Meijers, A., Bakker, R., Tiesinga, P. H. E., Morten, M., & Gerven, M. A. J. Van. (2017). The missing link : Predicting connectomes from noisy and partially observed tract tracing data. PLoS Computational Biology, 13(1), e1005374. (in collaboration with Bernhard Englitz and Max Hinne)
  • van Elteren, C., & Quax, R. (2019). The dynamic importance of nodes is poorly predicted by static topological features. ArXiv Preprint, arXiv:1904.06654. (in collaboration with Rick Quax)

Learning sensorimotor perception with spiking neural networks 


Mice use their whiskers to navigate their environment. The sensory information from mechanoreceptors in the skin travels through brain stem and thalamus to the so-called ‘barrel cortex’, the part of cortex that processes information from the whiskers in rodents. Even though we can make biophysical simulation models on the one hand, and purely phenomenological models describing how to extract sensory information on the other, the bridge between these two is difficult to make. ‘FORCE’ learning is a powerful algorithm, with which recurrent neural networks can learn to perform tasks. It has been implemented in spiking neural networks by amongst others Thalmeier et al. and Nicola and Clopath (see below). 


Using simultaneously recorded whisking and neural data from the department, and a spiking implementation of FORCE learning, you will make a biologically realistic implementation of a sensory perception task. 

  • Sussillo, D., & Abbott, L. F. (2009). Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron, 63(4), 544–557.
  • Nicola, W., & Clopath, C. (2017). Supervised learning in spiking neural networks with FORCE training. Nature Communications, 8(1), 1–15.
  • Thalmeier, D., Uhlmann, M., Kappen, H. J., & Memmesheimer, R. M. (2016). Learning Universal Computations with Spikes. PLoS Computational Biology, 12(6), 1–29.

Cell recruitment with external stimulation


In standard population excitability procedures, neurons are stimulated extracellularly. A baseline is defined by measuring the EPSP amplitude in postsynaptic neurons, and determining to current that is needed to evoke an EPSP with a certain amplitude. Next, the amplitude of the current injection is increased by steps relative to this baseline. This experiment is then repeated following certain procedures or pharmacological treatments, to infer network effects. However, if connections (pre to post) or excitability of neurons change, the baseline might not be maintained over conditions. So how should changes in EPSP amplitude/slope be interpreted?


In this project, you will model how cells get recruited as a function of the external stimulation amplitude,  i.e. you will make/use realistic neuron models (cell locations according to this model) and model how current spreads into a sphere. You will then model how cells get recruited. Finally, you will model how EPSPs in neighbouring columns are influenced by excitability and connectivity changes.

  • Joucla, S., Glière, A., & Yvert, B. (2014). Current approaches to model extracellular electrical neural microstimulation. Frontiers in Computational Neuroscience, 8(February), 1–12. doi:10.3389/fncom.2014.00013
  • Overstreet, C. K., Klein, J. D., & Helms Tillery, S. I. (2013). Computational modeling of direct neuronal recruitment during intracortical microstimulation in somatosensory cortex. Journal of Neural Engineering, 10(6). doi:10.1088/1741-2560/10/6/066016

Comparing methods of single-cell information transfer


Measuring the mutual information between the input into a neuron and its output spike train, helps us in determining the information-processing capabilities of the brain. However, it is notoriously difficult to measure mutual information, as during in-vitro experiments cells do not stay alive long enough (approximately one hour) to measure the full input-output probability distribution. Undersampled distributions typically give rise to strong biases. 


Several methods have been proposed to deal with the problem stated above. Using single neuron models, you will implement and compare these methods, and assess their robustness against short samples (low spike numbers) and their sensitivity to neuron types (i.e. type 1 and type 2 neurons). 

  • Dettner, A., Münzberg, S. & Tchumatchenko, T., 2016. Temporal pairwise spike correlations fully capture single-neuron information. Nature Communications, 7, pp.1–11.
  • Houghton, C., 2018. Calculating the Mutual Information Between Two Spike Trains. bioRxiv, p.423608.
  • Zeldenrust, F., de Knecht, S., Wadman, W. J., Denève, S., & Gutkin, B. (2017). Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series. Frontiers in Computational Neuroscience, 11, 49.

  • de Ruyter van Steveninck, R. R., & Bialek, W. (1988). Real-time performance of a movement-sensitive neuron in the blowfly visual system: coding and information transfer in short spike sequences. Proceedings of the Royal Society of London. Series B, 234(1277), 379–414. Retrieved from

  • Strong, S. P., Koberle, R., de Ruyter van Steveninck, R. R., & Bialek, W. (1998). Entropy and Information in Neural Spike Trains. Physical Review Letters, 80(1), 197–200.

Estimating the influence of top-down versus bottom-up modulation


The neural response in barrel cortex depends on the incoming bottom-up sensory input, but also in top-down input from other brain areas (higher-order layers and motor cortex). The trial-to-trial variability of the neural response therefore depends both on the type of coding that is used (rate versus temporal for instance), but also on the amount of feedback or top-down input to the network. In this project we will use both real and simulated data to estimate the amount of feedback input to barrel cortex.

Two subprojects
  1. Decoding

Using data from an online dataset (or other) we will use machine learning protocols to determine how well we can estimate several whisker input parameters (pole position, maximum curvature, whisker angle/phase upon contact) from the neural network response. In other words, we will train a classifier on the neural network data

The online Peron/Svoboda data include calcium recordings in head-fixed animals freely whisking against a pole with different positions. Each whisker contact-event, is determined by the pole location, whisker angle, angular velocity and phase upon contact and the maximum curvature of the contact. Due to the large difference in temporal resolution between the neural recordings and the whisker recordings, we can extract only a single neural population vector for each contact event. In this project we will train a (deep learning?) neural network to see how well the input parameters can be decoded from the population vector. This will give us an estimate on the coding precision of barrel cortex. 

  1. Simulations

Presently, we have a biophysical model of L4 and L2/3 of barrel cortex. This model receives thalamic spike trains as input, which is generated by filter neurons responding to whisker angle and curvature. The model does not include top-down or feedback networks, but each neuron can receive user-defined external input, mimicking this feedback input. So trial-to-trial variability in a given simulated network is the result of

    1. Spike timing precision of simulated thalamic spike trains
    2. Initial conditions
    3. Synaptic failures
    4. External input (mimicking feedback/top-down modulation)

We can vary each of these four sources of trial-to-trial variability and estimate their influence on the quality of the input representation by the network, using the methods of the decoding subproject. Given the results of this decoding subproject, we can estimate the strength of the influence of each of these four sources of variability (i.e. how they should be set so that the simulated network has a similar performance as the online data set) in real networks.


Estimating (un)certainty from neural recordings


In the gap-crossing task (Celikel&Sakmann, 2007), freely moving rodents locate a stationary tactile target in darkness. They only cross from one platform to the next, when they are certain enough of the position of the target. Recently, van Bergen and Jehee (2018) have developed a method to decode uncertainty from neural recordings.


In this project, you will use data from the department obtained during the gap-crossing task. You will adapt the method from van Bergen and Jehee (2018), which was initially developed for fMRI data, so that it can be used to 

  • van Bergen, R. S., & Jehee, J. F. M. (2018). Modeling correlated noise is necessary to decode uncertainty. NeuroImage, 180(August 2017), 78–87.

  • Celikel, T., & Sakmann, B. (2007). Sensory integration across space and in time for decision making in the somatosensory system of rodents. Proceedings of the National Academy of Sciences, 104(4), 1395–1400.

Biophysical modelling of the effects of the Kv1.1 potassium channel


Recent findings in our Department suggest that a molecule that controls cell-cycle is also critically involved in modulation on cortical excitability.  Whole cell current clamp studies suggest that this protein performs its action via changing the spike threshold in excitatory neurons; voltage clamp studies show that this change in spike threshold is associated with voltage gated sodium and potassium channels.  Molecular pathways analysis in single cell RNAseq data suggest the protein of interest is up-stream to Kv1.1, a prominent voltage gated potassium channel. 


This project will involve biophysical modeling in compartmental model neuron to determines the effects of this Kv1.1 potassium channel on the firing properties of pyramidal cells. The student will choose an existing model of a cortical pyramidal neuron, and develop a model of the Kv1.1 channel, add this to the cell model and verify the results with existing data from the lab. Next, the student will investigate the effects of the Kv1.1 density distribution on the firing properties. Finally, the results of this modelling study will be corroborated by in vitro experiments. The intended endpoint of this internship is a manuscript suitable for publication in a peer-reviewed journal.  

  • Stimberg, M., Goodman, D. F. M., Benichoux, V., & Brette, R. (2014). Equation-oriented specification of neural models for simulations. Frontiers in Neuroinformatics, 6.