Je suis directeur de recherche en neurosciences théoriques et computationnelles à l'Institut de la Vision où je dirige l'équipe de Neurosciences computationnelles des systèmes sensoriels. Je m'intéresse à la modélisation neuronale de la perception, à l'initiation des potentiels d'action, et également à l'épistémologie.
I am a research director in computational and theoretical neuroscience in the Vision Institute, where I lead the Computational neuroscience of sensory systems group. My main interests are neural modeling of perception and spike initiation. I also have an interest in epistemology.
Please don't hesitate to contact me if you are interested in working in my group.
1. Brette R (2015). Philosophy of the spike: rate-based vs. spike-based theories of the brain. I argue that the rate-based view of neural function is based on a number of misconceptions. Those mostly stem from viewing this question as a coding problem, whereas the actual issue is whether a description of neural function in terms of spike-based interactions may be reduced to a description in terms of rate-based interactions (a sort of thermodynamic limit). The possibility of such a reduction is unlikely, given our current neurophysiological knowledge.
2. Brette R (2015). What Is the Most Realistic Single-Compartment Model of Spike Initiation? I review experimental evidence demonstrating that, as far as spike initiation is concerned, the integrate-and-fire model is more realistic than the single-compartment Hodgkin-Huxley model – which described the space-clamped squid giant axon.
3. Bénichoux V, Fontaine B, Karino S, Franken TP, Joris PX*, Brette R* (2015). Neural tuning matches frequency-dependent time differences between the ears. We show that binaural neurons in the auditory brainstem do not measure time differences, but rather detect relations between monaural signals that are characteristic of spatial configurations.
4. Brette R (2013). Subjective physics. This is an epistemological paper where I describe the structure and properties of “subjective physics”, the laws that govern the interface of an organism with the world (sensory signals and actions). I propose that this is the “computational level” that is relevant to computational and systems neuroscience, rather than objective descriptions of the world meant for an external observer.
5. Brette R and Destexhe A, eds (2012). Handbook of Neural Activity Mesurement. Cambridge University Press.
7. Goodman, D. and R. Brette (2009). The Brian simulator.
Being a theoretical neuroscientist, I study the nervous system by means of designing, analyzing and simulating mathematical models. We use models and theories to connect two levels of understanding, for example molecular properties with neuron function, or neuron properties with behavior. The term computational in computational neuroscience refers to “how the brain computes”, i.e. the articulation between neuron properties and behavior, but the computer metaphor should not be taken too seriously. You may want to read a series of blog posts I wrote on the epistemology of theoretical and computational neuroscience. We have also recently started doing our own intracellular electrophysiological experiments.
I am currently interested in three broad topics: 1) the initiation of spikes (theoretical neuroscience); 2) sensory systems (computational neuroscience); 3) simulation technology (neuroinformatics). I have also worked on the design of intracellular recording techniques.
Vertebrate neurons communicate mainly by stereotypical electrical impulses called action potentials or “spikes” (see this series of posts on firing rate vs. spike timing). Thus a critical question is how neurons transform input signals into spike trains. At a general level, this is of course well known: sodium channels open when the membrane potential exceeds a threshold. But there are many subtleties. For example, the spike threshold depends on previous activity, on multiple timescales, with changes mediated by ionic channel properties (on a short timescale) and by structural changes (on a long timescale). Space also plays a critical role: I have recently shown theoretically that the axonal initiation of spikes makes sodium channels open as a discrete function of somatic voltage, effectively making the integrate-and-fire model much more realistic than previously thought (see this graphic explanation). There are also many unanswered questions, for example: how are the various ionic channels coordinated (in properties and in spatial distribution) so that spike initiation is functional and efficient? how is spike initiation modulated by activity on the long term? what is the function of the various types of channels in the axonal initial segment? For theoretical neuroscience, this is largely unexplored territory.
I am interested in how sensory systems work in ecological environments, and in particular in the perception of space, which is shared across almost all sensory modalities (including pain). Unlike lab environments, ecological environments are never simple (simple organisms also do not live in simple environments). My work starts from the view that perception relies on the identification and manipulation of models of the world, understood as relations between observables (ie, not necessarily generative models), where observables are sensory signals. I call these perceiver-oriented models “Subjective physics”. This view connects with major theories in psychology (Gestalt psychology, Gibson's ecological approach, O'Regan's sensorimotor theory), philosophy of mind (Poincaré, Merleau-Ponty) and linguistics (Lakoff).
Furthermore, I hypothesize that sensory relations are identified as temporal invariants in the sensory flow (ie relations that are satisfied over a contiguous period of time), which is most directly connected with Gibson's notion of “invariant structure”. Physiologically, I have proposed that relations between sensory signals are reflected in the relations between the timings of spikes, i.e., in neural synchrony that is tuned to specific sensory models. I have proposed the concept of “synchrony receptive field” to describe the set of sensory signals that elicit synchronous firing in a given set of neurons, together with neural network models that can identify sensory models based on selective synchrony. I have developed this idea mostly in the context of spatial hearing and pitch perception.
In 2008, I started the Brian simulator with Dan Goodman (postdoc at the time and now lecturer in Imperial College, UK). It is a simulator for spiking neural networks written in Python. The focus is on flexibility and ease of use, which has made it a highly popular tool in neuroscience. All models are directly specified by users with their equations – there are no predefined models, which has many benefits. It is also possible to simulate multicompartemental models. The new version (2.0), which is also developed by Marcel Stimberg, relies on code generation, which makes Brian much faster. We are currently working on running it on multiple types of hardware (collaborations are welcome).