My research area is computational neuroscience.
I use computational and mathematical techniques to understand the working of the brain. The hope is that, like in physics, it is possible to find simple underlying principles.
I am researching the computational consequences of realistic, spike timing dependent plasticity rules. In particular, the stability of memory and homeostasis have my current interest.
The nervous system has extrinsic and intrinsic noise sources. From an engineering point of view this leads to the question: how does the nervous system deal with this noise?
It turns out that the noise might be beneficial in some cases. More precisely, noise prevent synchronization (which reduces information content) and allows for fast and accurate propagation of signals.
Together with Rob Smith, I made a model of the rod-rodbipolar synapse.This is the first synapse in the visual pathway and is important at very low light levels.
In order to preserve signal quality, a special thresholding is required at this synapse. Some very good data on this system were recently measured.
We have also created a very realistic model of a ganglion cell with takes all known noise sources explicitly into account.
Billings G & van Rossum MCW. (2009). Memory Retention and Spike-Timing-Dependent Plasticity. Journal of Neurophysiology 101, 2775-2788.
Clark P, van Rossum M (2006) The optimal synapse for sparse, binary signals in the rod pathway. Neural Computation 18: 26-44
Janowitz MK, Van Rossum MCW (2006) Excitability changes that complement Hebbian learning. Network-Computation In Neural Systems 17: 31-41
van Rossum M, Renart A (2004) Computation with population codes in layered networks of integrate-and-fire neurons (CNS 2003, Alicante) Neurocomputing 58-60: 265-270
This article was published on Mar 29, 2010