After two years of work my last paper is finally out 17_05NECO. What I keep realizing is that science can take an awful long time. New idea need time to mature and even more time to be accepted. This paper was an uphill battle against editorial and peer reviewers. But it is finally out and a good excuse to write a new post.
This paper demonstrate an important thing. Dendrites can play a crucial role even for the most simple computation. The computation studied here is stimulus selectivity in other words a simple passing of information to signal the presence or absence of a particular input. Even in this case dendrites enables to make this computation more resilient to synaptic and dendritic failure. The implementation proposed here may seem too complex for a simple computation: to make the preferred inputs the most dispersed rather than the strongest. But it shows that with dendrites you can do more with less. Be more resilient with less synapses.
Another coming work will show that for the same computation an implementation employing dendrites use less synapses than a classic one. This becomes particularly interesting when one see that saturating dendrites can be implement using a resistor (that will saturate in all cases).
Some neurons respond preferentially to certain stimuli, like a sound or a picture reminding Jennifer Anniston. Hubel and Wiesel obtained the Nobel price, some 50 years ago, for the discovery of stimulus selective neurons in cats’ visual cortex and for the model associated with this discovery. This model shines by its simplicity. Imagine a mountain, each coordinates corresponds to a (visual) stimulus, the altitude associated to them corresponds to how strong a neuron responds to this stimulus. In more technical terms, the height of a given point equals the depolarization created by this stimulus. Now imagine that this mountain sits in the middle of a sea. The tip of the mountain outside of the water is the supra-threshold response, i.e. an activity level sufficient to trigger a neural activity noticeable by the rest of the brain. This metaphor seems a good way to understand neuron stimulus selectivity, but like all models it cannot explain everything.
Electrophysiology recently made astounding progresses and it is now possible to hyperpolarize a neuron in vivo. Using our metaphor it becomes feasible to increase the sea level. Scientists used this technique and made an unexpected observation. When they hyperpolarize a neuron -increase the sea level-, the mountain, as it goes underwater, becomes flat. Meaning that the neuron loses its selectivity and responds equally to all stimuli. Why? This is the topic of one of my current project. But I will talk more about it in another post, where I will try to explain why a neuron might start to respond as strong to Jennifer Anniston as to any other Hollywood inhabitants.
Motivated by my last post, I decided to more regularly update my blog.
In this post I am not asking a question but write about my life in Science and
my experience as a young researcher.
I enjoy a lot my life in Science, so I will try not to complain too much
(pledge), but life in science can sometimes be frustrating. I often
hear that the life of a young researcher can be difficult because of the
constant struggle. That he or she always has to fight against the existing
dogmas or ideas held by researchers higher up in the hierarchy. I tend to
disagree. Fighting against someone is motivating. You are never as
courageous as when you have a mighty opponent. Even a fierce and overwhelming
opponent that has much more means than you have. Worthwhile struggle is much
less frustrating than passive indifference. Science is just one area touched by
indifference and in our society overflowed by sounds, images and information
indifference sometimes is the only defense. So, I understand why people could
be indifferent. Still, I realize more and more that my frustration most often
comes from this indifference. Is there a solution? Well, it might be a clunky
solution but it is the only one I found. Indifference. I just say to myself
that if my work is worth something then someday, somewhere, somebody will use
it. Today I have a decent place to live, someone I love to live with, food in
my plate and people let me do what I love to do -Science. So no complain really.
I had the chance to give during the first week at OCNC (Okinawa Computational Neuroscience Course) a tutorial on NEURON neuron_tuto. This tutorial contains some self-advertisement ;), it demonstrates that a neuron with two passive dendrites can compute a linearly non-separable function, i.e. the feature binding problem. I hope it will be useful for your work.
Yesterday I had dinner with some friends, and to explain my work I used an analogy that they liked. I used this analogy many times to justify my point of view. I am convinced that neurons are smarter than we thought, way smarter, and that it should change our views on the brain. I spent my PhD trying to define “smart” and to demonstrate this intelligence (see review below). I believe that our brain is capable of amazing things, e.g. being goal directed, because neurons are capable of these amazing things. In other words, that a neuron is as complex as a brain. It may seem a weird opinion, but I found an analogy/question making this proposition less strange. Do you think that a society is more complex than a human? I do not think so. I think that a human/neuron is as complex as a society/brain (even if there are complex surely in different ways).
Our discovery -presented in this review wrote with some experimental collaborators Tran-van-Minh et al._2015– demonstrates that sublinear summation in dendrites makes a neuron more intelligent than we thought. And I am convinced that it is one step toward my proposition about neurons and humans.
I finally updated my publication list PubList. This is a satisfying thing to do, and it also makes me think about the way I walked and the way which remained. This way is well summarized in the over-ambitious title of this blog. Looking at my publication list I guess I am trying to come from both end of the spectrum with a preference for the single neuron side.
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 also submitted this work to a scientific journal! Fingers crossed for it, I hope it will go through to the review process at least.