An post to point toward the book chapter I wrote at the end of my PhD.Caze2013Chap

A neuron possesses receptive organs called dendrites that are active or passive. Active dendrites can sum excitatory inputs both below and above their arithmetic sum. This feature turns a neuron in a two-layer neural network capable of universal computation. Linearly separable computations previously define what a single neuron could do. Some neuron, however, lacks active dendrites. We thus wonder here what happens when dendrites are passive and can only sum two excitatory inputs below their arithmetic sum. We enumerate parameter sets and focus on excitatory inputs to determine how many computations can be implemented by a neuron model with either an active or passive dendrite. We then analytically generalize these numerical results to an arbitrary number of dendrites. First, we show that a single dendrite either passive or active suffices to compute linearly non-separable computations. Second, we analytically prove that a sufficient number of passive dendrites enable a neuron to be universal for positive computations. Third, we show how a neuron can implement these computations using two distinct strategies: 1) Where a single dendrite suffices to trigger a somatic spike; 2) where somatic spiking requires the cooperation of multiple dendrites. Only a neuron with active dendrites can use the strategy (1) while neuron with either passive or active dendrites can use strategy (2). Finally, we employ strategy (2) to implement a linearly non-separable function in a biophysical model with passive dendrites inspired by stellate cerebellar interneurons. We show here that even passive dendrites enable a neuron to extend its computation capacity well beyond what we previously thought.

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.

My very few followers might have wondered where I was (or not). Anyway, I have no answer to this question that could make a sensible story. I presented, however, a story that I very much like. I was in Crete for the dendrites 2014 meeting where I presented my work about on … dendrites and neural tuning. I changed my presentation at the last minute to remove the overlap with Dr Micheal Hausser presentation (mostly background). As you read me I am working on a manuscript to submit before the end of the year on this topic (eventually).

This is important to look at neural tuning because it is a known feature of the nervous system. Saying that: “dendrites tune neuron” is much more factual than “dendrites enable to compute linearly non-separable functions”. It is a step forward from my PhD work and make it a bit more concrete. Here is the abstract (2014Crete), I am certain you are impatient to read it!