About
I am a cognitive scientist with a focus on vision in a broad sense. I cherish biological and artificial perspectives of embodied vision and coaction between these two research fields. I have been affiliated with neuroscience modeling along with robotics labs in Portugal, Germany, and France. Consequently, my work spans from spiking data analysis to visual context classification. My latest work is in collaboration with Antoine Manzanera. We are working on lifelong learning of visual representations, particularly in the problems of incremental learning and curiosity. I am also a lecturer in computer vision, mathematics, informatics and cognitive science. I have taught in several french universities and BSc and MSc levels.Research Interests
Visual perception, early vision, biological modelling, unsupervised Learning, incremental learning, probabilistic models, information theory, attention, front-vision, retina, ecology, vision, sensory coding, embodyment, perception, intelligent systems, decision making.
Articles
Pamplona, D.; Hilgen, G.; Hennig, M.; Cessac, B.; Sernagor, E.; Kornprobst P.; Receptive field estimation in large visual neuron assemblies using a super-resolution approach, Journal of Neurophysiology, 2022 Code
Pamplona, D; Manzanera A.; Naturally Constrained Online Expectation Maximization,International Conference on Pattern Recognition (ICPR), 2020 Poster
Cessac, B; Kornprobst, P.; Kraria, S.; Nasser, H.; Pamplona, D.; Portelli, G.; Vieville T. PRANAS: A New Platform for Retinal Analysis and Simulation, Frontiers NeuroInformatics, 2017
Hilgen, G.; Pirmoradian, S.; Pamplona, D.; Kornprobst, P.; Cessac, B.; Hennig, M.H.; Sernagor E.; Pan-retinal characterisation of Light Responses from Ganglion Cells in the Developing Mouse Retina,Scientific Reports, 2017
Pamplona, D.; Triesch, J.; Rothkopf,C. A. ; Power spectra of the natural input to the visual system, Vision Research, 2013 Download code
Pamplona, D.; Bernardino, A.; Smooth Foveal Vision with Gaussian Receptive Fields, 9th IEEE - RAS International Conference on Humanoids Robots, 2009
Abstracts
Pamplona, D. ; Manzanera A. ; Should I stay or should I go? Addressing the curiosity/boredom dilemma of a domestic robot, international Workshop on Intrinsically Motivated Open-ended Learning, 2023,
Pamplona, D.; Manzanera A.; Uncertainty driven gaze selection, European Conference on Eye Movements (oral presentation), 2022
Pamplona, D.; Manzanera A.; Naturally Constrained Online Expectation Maximization, Conférence sur l'Apprentissage automatique, 2021
Cessac, B.; Kornprobst, P.; Kraria, S.; Nasser, H.; Pamplona, D.; Portelli, G.; Vieville T.; ENAS: A new software for spike train analysis and simulation, Bernstein Conference 2016
Hilgen, G.; Softley, S.; Pamplona, D.; Kornprobst, P.; Cessac, B.; Sernagor, E.; The effect of retinal GABA Depletion by Allylglycine on mouse retinal ganglion cell responses to light, European Retina Meeting, 2015
Pamplona, D.; Hilgen, G.; Cessac, B.; Sernagor, E.; Kornprobst, P.; A super-resolution approach for receptive fields estimation of neuronal ensembles, 24th Annual Computational Neuroscience Meeting (CNS), 2015
Pamplona, D.; Cessac, B.; Kornprobst, P.; Shifting stimulus for faster receptive fields estimation of ensembles of neurons, Computational and Systems Neuroscience (Cosyne), 2015
Pamplona, D.; Triesch, J.; Rothkopf,C.; Eye's imaging process explains ganglion cells anisotropies, Computational and Systems Neuroscience (Cosyne), 2013
Pamplona, D.; Triesch, J.; Rothkopf,C.; The statistics of looking: Deriving properties of retinal ganglion cells across the visual field, 12th Annual meeting of the Vision Sciences Society, 2012 (oral presentataion)
Pamplona, D.; Triesch, J.; Rothkopf,C.; Predicting Ganglion Cells Variability, Computational and Systems Neuroscience (Cosyne), 2011
Pamplona, D.; Triesch, J.; Rothkopf,C.; Edge and image statistics across the visual field, Bernstein Conference, 2011
Pamplona, D.; Weber, C.; Triesch J.;Foveation with optimized receptive fields, Bernstein Conference, 2009
Tushev G.; Liu, M.; Pamplona, D.; Bornschein, J.; Weber, C.; Triesch J.; Foveated Vision with FPGA Camera, Bernstein Conference, 2009 (demo)
Teaching
Since 2017, I am teaching several classes, namely: Introduction to Matlab, Visual Perception and Learning, Probabilities and Statistics III, Neuro-computational Models of Vision, Signal Processing, Methods Data and Algorithms.
Projects
Below is a list of possible short-term projects. This list is not exhaustive, it can be adapted to the collaborator interests and availability.Title | Topic | Key words | Students backgound | Length |
Incremental learning of images classes with Gaussian Mixture Models | Computer Vision | Incremental, unsupervised learning; image contexts | Machine Learning; Computer Vision; Applied Math;Probabilistics and Statistics | min: 10 weeks max: 6 months |
Truncated online Expectation Maximization | Machine Learning | Unsupervised online ML | Applied Math, Machine Learning | min: 10 weeks max: 6 months |
Pan retinal analysis of the aging impact of mouse RFs | Computational Neuroscience; | Reverse correlation; data analysis; fitting; visualization | Machine Learning; Biology; Neuroscience; | min: 10 weeks max: 3 months |
Design and set up of a robot with dual cameras | Robotics, Middle ware | robotics, middle ware | min: 3months max: 6months | |
On the consequences of Fisher Infomation Matrix diagonal approximation | Machine Learning/ probabilistics | FIM, computational time, approximation | Math, ML, engeneering | min: 10 weeks max: 6 months |
Model human motion primitives from videos | Computer Vision | Machine Learning/ probabilistics | ML, engeneering | min: 4 months max: 1 year |