Je suis directeur de recherche en neurosciences théoriques et computationnelles à l'Institut des Systèmes Intelligents et de Robotique (CV). Actuellement, je m'intéresse principalement à 1) la biophysique des potentiels d'action, 2) l'épistémologie des neurosciences, 3) la modélisation intégrative de la paramécie, vue comme un « neurone nageur ». J'écris occasionnellement sur la politique des sciences (une ou deux tribunes et un article de fond).
I am a research director in computational and theoretical neuroscience in the Institute of Intelligent Systems and Robotics (CV). These days, I am working on 1) biophysics of action potentials, 2) epistemology of neuroscience and 3) integrative modeling of Paramecium, the “swimming neuron”.
Please contact me if you are interested in working with me. There is currently a competitive call for PhD fellowships in neuroscience in Paris for international students, where I have a proposal on the neuroscience of escape behavior in Paramecium, the “swimming neuron”.
Latest paper: Elices et al. (2023). An electrophysiological and kinematic model of Paramecium, the "swimming neuron".
Vertebrate neurons interact mainly by stereotypical electrical impulses called action potentials (“spikes”), which are generally triggered in a small but highly organized structure called the axonal initial segment (AIS), next to the cell body. This structure undergoes structural plasticity: it moves, extends or shrinks with activity, development and pathologies. Why, how and to what effect? I have been developing a biophysical theory called resistive coupling theory, which provides a quantitative understanding of the relation between the structure and the electrical function of the AIS.
- Brette R (2013). Sharpness of spike initiation in neurons explained by compartmentalization. The foundational paper.
- Kole MHP and Brette R (2018). The electrical significance of axon location diversity. An accessible review.
- Goethals S, Brette R (2020). Theoretical relation between axon initial segment geometry and excitability. A detailed theoretical study.
- Fekete A, Ankri N, Brette R*, Debanne D* (2020). Neural excitability increases with axonal resistance between soma and axon initial segment. A key experimental test.
A striking fact about mainstream theories of the brain is that they take their inspiration mostly from theoretical computer science and engineering theory (“codes”, “computation”, “algorithms”, “information” (in bits), etc.), while being largely ignorant of theoretical biology (e.g. theories of life, organisms and evolution), and even dismissive of biology (just “implementation”). Over the years, I have developed a critical view of these mainstream theories. The key issue is the neglect of the processual nature of biological organisms at all time scales (metabolism, development, evolution). These conceptual flaws are sustained by confusion over polysemic words (state, information, representation, prediction, algorithms, etc.).
- Brette R (2019). Is coding a relevant metaphor for the brain? A critique of "neural codes".
- Brette R (2016). Subjective physics. (Now a chapter in Closed Loop Neuroscience, El Hady (ed), Academic Press.) A redefinition of the notion of information for an organism.
- Brette R (2015). Philosophy of the spike: rate-based vs. spike-based theories of the brain. A critique of rate-based theories of the brain, based on their erroneous identification of measurements of events (firing rates) with physical states.
Motivated (frustrated) by my epistemological work, I have decided to move away from the activity of making computational stories about neural correlates of various experimental situations, towards the integrative modeling of autonomous organisms. I have stumbled on Paramecium, a large unicellular eukaryote that swims in fresh water and controls its motility with action potentials. Paramecium swims, feeds, avoids, escapes, gathers, adapts, learns, develops. With a few collaborators, we do electrophysiological and behavioral experiments, genetics and modeling. We have developed a basic model of its action potential coupled to motility, and are now working on understanding the physiological basis of its proto-cognition.
- Brette R (2021). Integrative Neuroscience of Paramecium, a “Swimming Neuron”. An exhaustive review.
- Elices I, Kulkarni A, Escoubet N, Pontani LL, Prevost AM, Brette R (2023). An electrophysiological and kinematic model of Paramecium, the "swimming neuron". Our first integrative model.
4) Other work
Simulation technology. In 2008, I started the Brian simulator with Dan Goodman (postdoc at the time and now lecturer in Imperial College, UK). It is a popular simulator for spiking neural networks written in Python, now mainly developed and maintained by Marcel Stimberg, in my lab.
Electrophysiology. I invented a digital correction technique for single-electrode intracellular recording (AEC), and then worked on a couple of related techniques. Lately, I have developed some intracellular electrophysiology software in Python and experimental automation.
Sensory systems. I have been working on perception in ecological environments, in particular in the context of spatial hearing and pitch perception. This was (in hindsight) an effort to naturalize the Gibsonian concept of “invariant structure”, according to which the objects of perception are not arbitrary subsets of some vector space but laws followed by the sensory stream (the “subjective physics” of the world). I introduced the concept of the “synchrony receptive field”, as a neurobiological mechanism underlying the pick-up of these laws in some (very) simple contexts.
Motor control. As an effort to get rid of homuncular “decoders”, I got interested in motor control. The most interesting outcome of this investigation is the theoretical work done by my former student C. Le Mouel, showing that the logic of posture is better understood as anticipation than control. This is evident for example in the posture of a runner in starting blocks, but also applies to ordinary standing posture (in passing, a good illustration of the fact that anticipation is not prediction).