Section: Other Grants and Activities
Actions nationales
ARC MACACC Modélisation de l’Activité Corticale et Analyse du Code neural Cérébral.
Keywords : spike train statistics, mesoscopic models of cortical columns.
Participants : Bruno Cessac, Maureen Clerc, Olivier Faugeras, Pierre Kornprobst, Theodore Papadopoulo, Horacio Rostro, Jonathan Touboul, Juan-Carlos Vasquez.
Duration:January 2008 to December 2009
This project involves the following partners : The INRIA project teams ODYSSEE, ALCHEMY, CORTEX the Institut de Neurosciences Cognitives de la Méditerranée (INCM-Dyva), and the Laboratoire de Mathématiques Jean-Alexandre Dieudonné (Nice University). It is jointly founded by an ARC INRIA and the Doeblin fundation. Neuronal information processing is related to the brain bio-electrical activity. Current neuro imaging techniques allow the measurement of this bio-electric activity at different time and space scales, from neurons to the brain as a whole (e.g. LFP, ECoG, EEG, MEG). But the analysis of data coming from these measures requires the parallel development of suitable models. Namely, these models have to be, on one hand, close enough to phenomenology, taking into account the various type of bio-electrical activity and their scales relations, in order to propose a coherent representation of information processing in the brain (from neurons to neuronal populations, cortical columns, brain area, etc). On the other hand, these models must be well posed and analytically tractable. This requires a constant interaction between neurobiology, modelling and mathematics. In this spirit, this project aims to tackle the following questions, combining results from neuroscience, dynamical systems theory and statistical physics.
-
Statistical models of spikes trains. The analysis of experimental data, in vivo or in vitro, of spike trains, requires suitable statistical models. The models typically used (e.g. Poisson) are ad hoc and may not be adapted to all situations. Our goal is to propose a generic method to construct the probability distribution of spikes trains, using an approach combining mathematical modelling and analysis and in vivo experiments, together with numerical simulations.
-
Mesoscopic models of cortical columns. Brain imaging techniques, like optical imaging, require a modelling of cortical brain activity at a space scale of order 0.1-1 mm2. The goal is, on the theoretical side, to propose a mesoscopic model of the biological signal measured in optical imaging, at the space scale of a cortical column, and to analyse this model, using analytical methods and numerical simulations. This model will be then compared to the cortical activity of the visual system (area V1-V2), measured by optical imaging.
Website: http://www-sop.inria.fr/neuromathcomp/contracts/arc_macacc
ANR HR-CORTEX
Participants : Romain Brette, Olivier Faugeras, Easwar Subramanian.
Duration:1st December 2006 to 30th November 2009
This project combines different expertises, such as mathematics, computer science, computational neuroscience and electrophysiology (in vitro and in vivo), to yield accurate and reliable methods to properly characterize high-conductance states in neurons. The partners in this project are Odyssée and UNIC (CNRS - Gif-sur-Yvette, France). We plan to address several of the caveats of present recording techniques, namely (1) the impossibility to perform reliable high-resolution dynamic-clamp with sharp electrodes, which is the intracellular technique mostly used in vivo; (2) the unreliability and low time resolution of single-electrode voltage-clamp recordings in vivo; (3) the impossibility of extracting single-trial conductances from Vm activity in vivo. We propose to address these caveats with the following goals:
-
Obtain high-resolution recordings applicable to any type of electrode (sharp and patch), any type of protocol (current-clamp, voltage-clamp, dynamic-clamp) and different preparations (in vivo, in vitro, dendritic patch recordings).
-
Obtain methods to reliably extract single-trial conductances from Vm activity, as well as to “probe” the intrinsic conductances in cortical neurons. These methods will be applied to intracellular recordings during visual responses in cat V1 area in vivo.
-
Obtain methods to extract correlations from Vm activity and apply these methods to intracellular recordings in vivo to measure changes in correlation in afferent activity.
-
Obtain methods to estimate spike-triggered averages from Vm activity and obtain estimates of the optimal patterns of conductances that trigger spikes in vivo. These results will be integrated into computational models to test mechanisms for selectivity.
In all of these methods, we take advantage of the real-time feedback between a computer and the recorded neuron. This real-time feedback will be used to (a) design a new type of recording paradigm, which we call Active Electrode Compensation (AEC), and which consists in a real-time computer-controlled compensation of the electrode artefacts and bias which currently limit recording precision; (b) to use the AEC method to improve current-clamp, voltage-clamp and dynamic-clamp recordings of cortical neurons; (c) use this method as an essential tool to design methods for estimating conductances and statistical characteristics of network activity from intracellular recordings.
Thus, we expect this project to provide three main contributions: (1) It will provide technical advances in the precision and resolution of several currently-used recording techniques, such as dynamic-clamp and voltage-clamp, which are currently limited. We aim at obtaining high-resolution (>= 20 KHz) reliable measurement or conductance injection. This advance should be of benefit for in vivo and in vitro electrophysiologists. (2) It will enable us to perform high-resolution conductance measurements in high-conductance states in vivo and in vitro and better understand this type of network activity. (3) It will enable us to better understand the spike selectivity of cortical neurons, by directly measuring single-trial conductances underlying visual responses, as well as the conductance time courses linked to the genesis of spikes. Those measurements will be directly integrated into computational models. The mechanisms of spike selectivity in cortical neurons is still a subject of intense debate, and we expect to provide here crucial measurements, which we hope will help us better understand input selectivity in visual cortex.
