I presented this work for the first time in Paris. I undertook it in collaboration with Dr Claudia Clopath and Dr Simon Schultz and it was presented at a workshop on dendritic computation at the new EITN in Paris.
In this work we present a neuron model that can display both synaptic clustering and scattering. Simultaneously active synapses cluster if they are co-localized on the same dendritic branch (in a ~10-20 microns radius on dendrites). We say that active synapses scatter when they are located on different dendrites. Both observations are possible in the same type of neuron, e.g. a pyramidal neuron from the upper layer (II/III) of the cortex. In this work we conciliate the two sets of observations. In a sentence, we demonstrate, with a model, that clustered synapses could be useful during learning while scattered synapses are useful during sensing. This goes well with the fact that scattered synapses are observed more during a sensory-evoked episode.
This work is important to me because I have shown during my PhD that dendrites enables to compute amazing things (linearly non-separable computations). In other words, I showed that a plane (neuron) can fly high. Here I am showing how this plane can gain altitude (plasticity). With this work I am showing how a neuron can learn to do these amazing things and also I am explaining some strange pieces of experimental data, killing two birds with one stone.
I recently submitted this work to the Cosyne conference where it was accepted (the extended abstract is here 14_11CosyneA). I am looking forward to present this work in Salt Lake City!
I also submitted this work to a scientific journal! Fingers crossed for it, I hope it will go through to the review process at least.
Romain Cazé 