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.

This work started in a summer school in Okinawa where I had the chance to work with Matthijs Van Der Meer. Matt was the tutor of my project, originally this project was between Bayes and Pavlov but during our first gathering things changed a little bit. We were a group of student all with different projects and we began by introducing ourselves; Tali Sharot was one of the student, she talked about her work on optimism and her studies on the subject. She had already published a Nature paper on the subject in which she suggested that optimism, despite being suboptimal, exists to cope with the difficulty of our lifes (she also later gave a TED talk on the “optimism bias”). But “What if optimism was optimal?”, and this is how my project changed. She did not want to collaborate with us, but we successfully worked on this project with Matt.

Our work is showing that in certain situations optimism and even pessimism can be optimal features (the cosyne abstract:Cosyne2010B, the cosyne poster:Cosyne2010Bposter, and the Biological cybernetic aritcle:BioCyb2013). This theoretical study demonstrates that a differential learning rate for reward and punishment can generate optimism/pessimism that is a kind of classic and expected result. The originality of this study is to show that optimism/pessimism creates a distortion in how we evaluate our choices, and that this distortion is inhomogeneous turning tough choices into easy ones.

I enjoy working on this topic, as it is showing that human “defects” are indeed optimal. The aspect I like the most is the demonstration that pessimism can be optimal. In a situation where you are rewarded all the time, it yields more reward to be pessimist because the subject is more able to dissociate highly rewarded choices. This is a notable difference other studies where pessimism is clearly badly connoted. This is also easier to explain to my family than my work on dendrites, e.g. I wrote for fun my first outreach  text published in a newsletter of our bioengineering department (September 2013).