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.
Are you certain that the immune system is the main target of vaccines? This
question came to my mind quite late yesterday evening, so please be indulgent.
I thought about a colleague who yesterday inoculated a transgenic construct
using a virus. She modified the genome of certain cells to make them
fluorescent. This technique is amazing but quite standard. And we all learn at
school that vaccines modify the immune system which form specific antigens
against certain pathogen. So why my question might be interesting? What if the
vaccines mildly affect the immune system and instead modify the cells, e.g.
their genome, that are usually targeted by the virus? What if Pasteur was
creating GMOs long before it was discovered? For instance the inactivated
rabies vaccine might modify many cells; consequently when the true rabies
virus is in contact with these cells, it can no longer infect them.
Most likely, college students are going to tell me that my question has an
evident “yes” answer. But some scientists might (and might have already) think
about it. They could even try to answer this question with an experiment. By
looking at the effect of a modified vaccine – to which a fluorescent protein
construction is attached – on mammalian cells. This modified vaccine would be
as efficient as a normal vaccine but would show (or not) that the genome of
the cells usually targeted by the virus changed. This modified vaccine could
sometimes fail and observing fluorescence only in a working case would
strengthen a “no”. Answering my question by a “no” would dramatically change
things. At minimum it would change my own understanding of immunology.
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.