FACETS (2006/2010)

List of publications that were funded by the FACETS project (more info).

  • also available on the FACET’s website

See also:

Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.


2010-06-22 : CodeJamNr4

We held a CodeJam 22nd-24th June 2010, in Marseille.


Complex dynamics in recurrent cortical networks based on spatially realistic connectivities

Most studies on the dynamics of recurrent cortical networks are either based on purely random wiring or neighborhood couplings. …

Phase space analysis of networks based on biologically realistic parameters

We study cortical network dynamics for a spatially embedded network model. It represents, in terms of spatial scale, a large piece of …

Functional consequences of correlated excitatory and inhibitory conductances in cortical networks

Neurons in the neocortex receive a large number of excitatory and inhibitory synaptic inputs. Excitation and inhibition dynamically …

Phase space analysis of networks based on biologically realistic parameters

We study cortical network dynamics for a more realistic network model. It represents, in terms of spatial scale, a large piece of …

Decoding center-surround interactions in population of neurons for the ocular following response

Short presentation of a large moving pattern elicits an Ocular Following Response (OFR) that exhibits many of the properties attributed …

Dynamical state spaces of cortical networks representing various horizontal connectivities

Most studies of cor tical network dynamics are either based on purely random wiring or neighborhood cou- plings, e.g., [Kumar, …

Dynamics of cortical networks including long-range patchy connections

Most studies of cortical network dynamics are either based on purely random wiring or neighborhood couplings [1], focussing on a rather …

Functional consequences of correlated excitation and inhibition on single neuron integration and signal propagation through synfire chains

Neurons receive a large number of excitatory and inhibitory synaptic inputs whose temporal interplay determines their spiking behavior. …

Inferring monkey ocular following responses from V1 population dynamics using a probabilistic model of motion integration

Short presentation of a large moving pattern elicits an ocular following response that exhibits many of the properties attributed to …

Dynamics of distributed 1D and 2D motion representations for short-latency ocular following

Integrating information is essential to measure the physical 2D motion of a surface from both ambiguous local 1D motion of its …

Adaptive Sparse Spike Coding : applications of Neuroscience to the compression of natural images

If modern computers are sometimes superior to cognition in some specialized tasks such as playing chess or browsing a large database, …

PyNN: A Common Interface for Neuronal Network Simulators

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and …

PyNN: towards a universal neural simulator API in Python

Trends in programming language development and adoption point to Python as the high-level systems integration language of choice. …


Reading out the dynamics of lateral interactions in the primary visual cortex from VSD data

Short presentation of a large moving pattern elicits an ocular following response that exhibits many of the properties attributed to low-level motion processing such as spatial and temporal integration, contrast gain control and divisive interaction between competing motions. Similar mechanisms have been demonstrated in V1 cortical activity in response to center-surround gratings patterns measured with real-time optical imaging in awake monkeys (see poster of Reynaud et al., VSS09). Based on a previously developed Bayesian framework, we have developed an optimal statistical decoder of such an observed cortical population activity as recorded by optical imaging. This model aims at characterizing the statistical dependance between early neuronal activity and ocular responses and its performance was analyzed by comparing this neuronal read-out and the actual motor responses on a trial-by-trial basis. First, we show that relative performance of the behavioral contrast response function is similar to the best estimate obtained from the neural activity. In particular, we show that the latency of ocular response increases with low contrast conditions as well as with noisier instances of the behavioral task as decoded by the model. Then, we investigate the temporal dynamics of both neuronal and motor responses and show how motion information as represented by the model is integrated in space to improve population decoding over time. Lastly, we explore how a surrounding velocity non congruous with the central excitation information shunts the ocular response and how it is topographically represented in the cortical activity. Acknowledgement: European integrated project FACETS IST-15879.

Decoding low-level neural information to track visual motion

Moving the eyes rapidly to track a visual object moving in a cluttered environment is an essential function. However, doing so rapidly and efficiently is constrained by a number of noise sources in the visual system and by the fact that information is collected locally before giving raise to a global signal. After reviewing some results made in the modeling of low-level sensory areas, I will expose a method to decode low-level neural information as describing visual information using a probabilistic representation. Decisions will therefore correspond to statistical inferences which are dynamically resolving the veridical speed of a moving object. We will illustrate this method by showing how ambiguous local information can be merged to give raise to a global response which resolves the aperture problem. Using this theoretical approach \“in computo\“, we will illustrate how we may better understand results which are observed \“in vivo\” (optical imaging) as a neural code linking actively sensation and behavior.

Decoding the population dynamics underlying ocular following response using a probabilistic framework

The machinery behind the visual perception of motion and the subsequent sensorimotor transformation, such as in Ocular Following Response (OFR), is confronted to uncertainties which are efficiently resolved in the primate’s visual system. We may understand this response as an ideal observer in a probabilistic framework by using Bayesian theory (Weiss et al., 2002) which we previously proved to be successfully adapted to model the OFR for different levels of noise with full field gratings or with disk of various sizes and the effect of a flickering surround (Perrinet and Masson, 2007). More recent experiments of OFR have used disk gratings and bipartite stimuli which are optimized to study the dynamics of center-surround integration. We quantified two main characteristics of the global spatial integration of motion from an intermediate map of possible local translation velocities: (i) a finite optimal stimulus size for driving OFR, surrounded by an antagonistic modulation and (ii) a direction selective suppressive effect of the surround on the contrast gain control of the central stimuli (Barthelemy et al., 2006, 2007). Herein, we extended in the dynamical domain the ideal observer model to simulate the spatial integration of the different local motion cues within a probabilistic representation. We present analytical results which show that the hypothesis of independence of local measures can describe the initial segment of spatial integration of motion signal. Within this framework, we successfully accounted for the dynamical contrast gain control mechanisms observed in the behavioral data for center-surround stimuli. However, another inhibitory mechanism had to be added to account for suppressive effects of the surround. We explore here an hypothesis where this could be understood as the effect of a recurrent integration of information in the velocity map. F. Barthelemy, L. U. Perrinet, E. Castet, and G. S. Masson. Dynamics of distributed 1D and 2D motion representations for short-latency ocular following. Vision Research, 48(4):501–22, feb 2007. doi: 10.1016/j.visres.2007.10.020. F. V. Barthelemy, I. Vanzetta, and G. S. Masson. Behavioral receptive field for ocular following in humans: Dynamics of spatial summation and center-surround interactions. Journal of Neurophysiology, (95):3712–26, Mar 2006. doi: 10.1152/jn.00112.2006. L. U. Perrinet and G. S. Masson. Modeling spatial integration in the ocular following response using a probabilistic framework. Journal of Physiology (Paris), 2007. doi: 10.1016/j.jphysparis.2007.10.011. Y. Weiss, E. P. Simoncelli, and E. H. Adelson. Motion illusions as optimal percepts. Nature Neuroscience, 5(6):598–604, Jun 2002. doi: 10.1038/nn858.

From neural activity to behavior: computational neuroscience as a synthetic approach for understanding the neural code.

Computational Neuroscience is a synthetic, inter-disciplinary approach aiming at understanding cognition by analyzing the mechanisms underlying neural computations. We present in this seminar our attempt in modeling low-level vision by bridging different integration levels, from neural spiking activity to behavior. At the behavioral level, the Ocular Following Response recorded in the laboratory reveals how the brain may integrate local information (moving images on visual receptive fields) to produce a single behavioral response (the movement of the eye). Using a probabilistic representation, we provide a simple integrative mechanism that gives the “ideal” response to possibly noisy and ambiguous information, similarly to a Bayesian approach. This fits well the performance revealed by behavioral data and may act as a generic cortical “module”. At the population level, these mechanisms may indeed be implemented for the coding of natural images and we will show the particular importance of spiking representations and lateral interactions for efficient and rapid responses. In particular, we will present an original unsupervised learning algorithm that we applied to a model of the primary visual cortex. Finally, at the neuronal level, I will present work done in the team showing how certain mechanisms at the level of the synapse and of the neuron are essential at the population level and how we may understand these mechanisms at the population level. This illustrates the importance of dynamical processes, distributed activity and recurrent connections to produce a cortical gain control mechanism. As a conclusion, this approach provides useful applications for image processing and possible valorization in future computer architectures. More generally, it proves that the use of a probabilistic representation is a particularly efficient method for bridging biological versus computational neuroscience and illustrates the advantage of such an interdisciplinary approach.