About MAGMA Team
The retina encodes the visual information of the environment from an input of photons flux to a series of electrical pulses that are ultimately read out by the brain to create perception and program motor actions. This process can be seen as a series of parallel circuits, computing visual features from the environment, and encoding the mere informative inputs sent to the brain. Among all these visual features, motion processing represents a fundamental visual computation ruling many visuomotor behaviors with a large pool of capabilities: detecting differential motion, direction selectivity and predictive coding, i.e., an anticipatory response of the visual system when an object follows a trajectory in the visual field.
MAGMA (Modelling And understandinG Motion Anticipation in the retina) Team looks forward to studying the mechanisms underlying anticipatory response and the predictive coding observed in the mammalian retina, with a particular emphasis on lateral connectivity (amacrine cells and gap junctions). Based on the importance of understanding how the visual system accumulates information and resolving problems inherent from neural computation, its dynamics, and implementations for simulations.
These advancements and new understandings could be used for compensating visual impairment, with technology knowing how does the visual channel interprets the visual stimulus as neural signals. And could be extended even to new algorithms of image/video processing and autonomous robot navigation.
People
- Bruno Cessac – Team leader of the Biovision Lab. French coordinator of the MAGMA associated team
- Pierre Kornprobst – Biovision Lab
- Jenny Kartsaki – Biovision Lab
- Simone Ebert – Biovision Lab
- Maria-José Escobar – Chilean coordinator of the MAGMA associated team – Advanced Center for Electrical and Electronic Engineering (AC3E), Department of the Universidad Técnica Federico Santa María, Valparaíso, Chile.
- Rodrigo Cofré – Universidad de Valparaiso (UV), Centro de Investigación y Modelamiento de Fenómenos Aleatorios (CIMFAV) and Instituto de Ingeniería Matemática UV, Valparaíso, Chile
- Adrián Palacios – Principal Investigator at Centro Interdisciplinario de Neurociencia de Valparaíso – Full Professor at Faculty of Sciences, Universidad de Valparaíso
- Ignacio Ampuero – Universidad Técnica Federico Santa María (UTFSM), Valparaíso, Chile
Our Interests
| Continuous Modeling (PDE, ODE) | Bioinformatics |
| Stochastic Modeling (SPDE, SDE) | Mathematical biology |
| Multiscale modeling | Neuroscience and cognitive science |
| Statistical methods | Understanding and simulation of the brain and the nervous system |
Research Goals
The proposal expects to study and characterize, from an experimental, functional and theoretical point of view, the anticipatory response observed in the mammalian retina confronted to a moving object following a trajectory. This characterization will allow us to define the predictive coding capabilities present in the retina. The work is organized into four goals:
- To experimentally characterize motion selectivity properties in the retina of a diurnal (Octodon degus) and nocturnal (Mus musculus) rodents.
- To characterize the anticipatory response observed in the retina to motion trajectories, and its predictive coding capabilities, both in nocturnal and diurnal rodents.
- To propose a functional mechanism and a mathematical model to explain the anticipatory response observed in the retina. Especially, to investigate the role of the lateral connectivity (via amacrine cells and gap junctions) and how spatio-temporal correlations in motion are converted into spike spatio-temporal correlations deciphered by the brain to anticipate motion.
- To link these results to applications in the domain of assisted vision.
MAGMA Team Progress
Reports
- Ignacio Ampuero’s Internship report
Research papers
- Ignacio Ampuero, Rodrigo Cofré, Bruno Cessac, Linear response for spiking neuronal networks with unbounded memory, Entropy, MDPI, 2021, 23 (2), pp.155. ⟨10.3390/e23020155⟩. Abstract. We establish a general linear response relation for spiking neuronal networks, based on chains with unbounded memory. This relation allow us to predict the influence of a weak amplitude time dependent external stimuli on spatio-temporal spike correlations, from the spontaneous statistics (without stimulus) in a general context where the memory in spike dynamics can extend arbitrarily far in the past. Using this approach, we show how the linear response is explicitly related to the collective effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike train statistics. We illustrate our results with numerical simulations performed over a discrete time integrate and fire model.
- R. Cofré, C. Maldonado, B. Cessac, “Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics“, Entropy 2020, 22, 1330. Abstract: The Thermodynamic Formalism provides a rigorous mathematical framework for studying quantitative and qualitative aspects of dynamical systems. At its core, there is a variational principle that corresponds, in its simplest form, to the Maximum Entropy principle. It is used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science. In particular, it has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, in order to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.
Contribution to software development
- Sebastian Gallardo-Diaz has contributed to the INRIA software Macular.
Planned Activities
- Integrate the retino-cortical model developed by S. Souihel thesis in the Macular retina simulator.
- Compute the correlations induced by a moving bar from the recorded spontaneous activity using linear response theory
- Develop mathematical methods to reduce the dimensionality of Maximum Entropy models used to fit retina data.
- Optimizing newspaper layout with design-preserving magnification: Study of a new combinatorial/geometric packing problem (Chilean student: S. Gallardo, supervisors: P. Kornprobst and D. Mazauric).
