6.1.1 Macular

• Name:
  Numerical platform for simulations of the primary visual system in normal and pathological conditions
• Keywords:
  Retina, Vision, Neurosciences
• Scientific Description:

  At the heart of Macular is an object called "Cell". Basically these "cells" are inspired by biological cells, but it's more general than that. It can also be a group of cells of the same type, a field generated by a large number of cells (for example a cortical column), or an electrode in a retinal prosthesis. A cell is defined by internal variables (evolving over time), internal parameters (adjusted by cursors), a dynamic evolution (described by a set of differential equations) and inputs. Inputs can come from an external visual scene or from other synaptically connected cells. Synapses are also Macular objects defined by specific variables, parameters, and equations. Cells of the same type are connected in layers according to a graph with a specific type of synapses (intra-layer connectivity). Cells of a different type can also be connected via synapses (inter-layer connectivity).

  All the information concerning the types of cells, their inputs, their synapses and the organization of the layers are stored in a file of type .mac (for "macular") defining what we call a "scenario". Different types of scenarios are offered to the user, which they can load and play, while modifying the parameters and viewing the variables (see technical section).

  Macular is built around a central idea: its use and its graphical interface can evolve according to the user's objectives. It can therefore be used in user-designed scenarios, such as simulation of retinal waves, simulation of retinal and cortical responses to prosthetic stimulation, study of pharmacological impact on retinal response, etc. The user can design their own scenarios using the Macular Template Engine (see technical section).

• Functional Description:
  Macular is a simulation platform for the retina and the primary visual cortex, designed to reproduce the response to visual stimuli or to electrical stimuli produced by retinal prostheses, in normal vision conditions, or altered (pharmacology, pathology, development).
• Release Contributions:
  First release.
• News of the Year:
  First release, APP
• URL:
  https://­team.­inria.­fr/­biovision/­macular-software/
• Contact:
  Bruno Cessac
• Participants:
  Bruno Cessac, Evgenia Kartsaki, Selma Souihel, Teva Andreoletti, Alex Ye, Sebastian Gallardo Diaz, Ghada Bahloul, Tristan Cabel, Erwan Demairy, Pierre Fernique, Thibaud Kloczko, Come Le Breton, Jonathan Levy, Nicolas Niclausse, Jean-Luc Szpyrka, Julien Wintz, Carlos Zubiaga Pena

6.1.2 GUsT-3D

• Name:
  Guided User Tasks Unity plugin for 3D virtual reality environments
• Keywords:
  3D, Virtual reality, Interactive Scenarios, Ontologies, User study
• Functional Description:

  We present the GUsT-3D framework for designing Guided User Tasks in embodied VR experiences, i.e., tasks that require the user to carry out a series of interactions guided by the constraints of the 3D scene. GUsT-3D is implemented as a set of tools that support a 4-step workflow to :

  (1) annotate entities in the scene with names, navigation, and interaction possibilities, (2) define user tasks with interactive and timing constraints, (3) manage scene changes, task progress, and user behavior logging in real-time, and (4) conduct post-scenario analysis through spatio-temporal queries on user logs, and visualizing scene entity relations through a scene graph.

• Contact:
  Hui-Yin Wu
• Participants:
  Hui-Yin Wu, Marco Alba Winckler, Lucile Sassatelli, Florent Robert
• Partner:
  I3S

6.1.3 PTVR

• Name:
  Perception Toolbox for Virtual Reality
• Keywords:
  Visual perception, Behavioral science, Virtual reality
• Functional Description:
  Some of the main assets of PTVR are: (i) The “Perimetric” coordinate system allows users to place their stimuli in 3D easily, (ii) Intuitive ways of dealing with visual angles in 3D, (iii) Possibility to define “flat screens” to replicate and extend standard experiments made on 2D screens of monitors, (iv) Save experimental results and behavioral data such as head and gaze tracking. (v) User-friendly documentation with many animated figures in 3D to easily visualize 3D subtleties, (vi) Many “demo” scripts whose observation in the VR headset is very didactic to learn using PTVR, (vii) Focus on implementing standard and innovative clinical tools for visuomotor testing and visuomotor readaptation (notably for low vision).
• News of the Year:
  The first release of PTVR was presented in August 2022 at the conference ECVP 21
• URL:
  https://­ptvr.­inria.­fr
• Authors:
  Eric Castet, Christophe Hugon, Jeremy Termoz-Masson, Johanna Delachambre, Hui-Yin Wu, Pierre Kornprobst
• Contact:
  Pierre Kornprobst
• Partner:
  Aix-Marseille Université - CNRS Laboratoire de Psychologie Cognitive - UMR 7290 - Team ‘Perception and attention’

6.2.1 CREATTIVE3D platform for navigation studies in VR

Participants: $☆$ Hui-Yin Wu (coordinator), Lucile Sassatelli, Marco Winckler, $☆$ Florent Robert (PhD student and lead developer).

As part of ANR CREATTIVE3D, the Biovision team has established a techonological platform in the Kahn immersive space including:

• a 40$m^2$ tracked space for the Vive Pro Eye virtual reality headset with 4-8 infra-red base stations.
• GUsT-3D software 6.1.2 under Unity for the creation, management, logging, analysis, and scene graph visualization of interactive immersive experiences
• Vive Pro Eye headset integrated sensors including gyroscope and accelerometers, spatial tracking through base stations, and 120 Hz eye tracker
• External sensors including XSens Awinda Starter inertia-based motion capture costumes and Shimmer GSR physiological sensors

7.1.1 Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

Participants: $☆$ Simone Ebert, Thomas Buffet${}^1$, Semihcan Sermet${}^1$, Olivier Marre${}^1$, $☆$ Bruno Cessac.

1 Institut de la Vision, Sorbonne Université, Paris, France.

Description: A long standing hypothesis is that retinal ganglion cells, the retinal output, do not signal the visual scene per se, but rather surprising events, eg. mismatches between observation and expectation formed by previous inputs. A striking example of this is the Omitted Stimulus Response (OSR): when a regular sequence of flashes suddenly ends, the retina emits a large response signaling the missing stimulus, and the latency of this response shifts with the period of the flash sequence to respond to the omitted stimulus. However, the mechanism behind this predictive latency shift remains unclear. Here we show that inhibition is necessary for this latency shift. Using a combination of modeling and experiments, we show that latency shift of the OSR in ganglion cells is obtained by amacrine cells with inhibitory synapses. Inhibition delays the response at the end of the sequence, and the depressing synapse shifts this delay as a function of the frequency of the flash sequence. High frequency sequence induce a strong depression of the inhibitory synapse, a weak inhibitory input and thus a small increase in the latency, while low frequency inputs induce a small depression of the inhibitory synapse, a strong inhibitory input and therefore a large increase in latency. We build a circuit model that reproduces our experimental findings and generates new predictions, that we confirm by further experiments. Since depressing inhibitory synapses are ubiquitous in sensory circuits, our results suggest they could be a key component to generate the predictive responses that have been observed in several brain areas.

This figure shows a schematic of the retina, experimental recordings of the Omitted Stimulus Response.in normal conditions and under application of strychnine which blocks glycine.

This work has been presented in the conferences ICMNS 2022 – 2022 International Conference on Mathematical Neuroscience (on line) 27, NEUROMOD Meeting 2022 (Antibes) 32 AREADNE 2022 (Santorini, Greece) 31, Dendrites 2022 – Dentritic, anatomy, molecules and functions – Embo workshop, (Heraklion, Greece) 30 and will be soon submitted to the journal eLife.

7.1.2 Receptive field estimation in large visual neuron assemblies using a super resolution approach

Participants: Daniela Pamplona${}^1$, Gerrit Hilgen${}^2$, Matthias H. Hennig${}^3$, $☆$ Bruno Cessac, Evelyne Sernagor${}^2$, $☆$ Pierre Kornprobst.

1 Ecole Nationale Supérieure de Techniques Avancées, Institut Polytechnique de Paris, France

2 Institute of Neuroscience (ION), United Kingdom

3 Institute for Adaptive and Neural Computation, University of Edinburgh, United Kingdom

Description: Computing the Spike-Triggered Average (STA) is a simple method to estimate linear receptive field (RF) in sensory neurons. For random, uncorrelated stimuli the STA provides an unbiased RF estimate, but in practice, white noise at high resolution is not an optimal stimulus choice as it usually evokes only weak responses. Therefore, for a visual stimulus, images of randomly modulated blocks of pixels are often used. This solution naturally limits the resolution at which an RF can be measured. Here we present a simple super-resolution technique that can be overcome these limitations. We define a novel stimulus type, the shifted white noise (SWN), by introducing random spatial shifts in the usual stimulus in order to increase the resolution of the measurements. In simulated data we show that the average error using the SWN was 1.7 times smaller than when using the classical stimulus, with successful mapping of 2.3 times more neurons, covering a broader range of RF sizes. Moreover, successful RF mapping was achieved with brief recordings of light responses, lasting only about one minute of activity, which is more than 10 times more efficient than the classical white noise stimulus. In recordings from mouse retinal ganglion cells with large scale multi-electrode arrays, we successfully mapped 21 times more RF than when using the traditional white noise stimuli. In summary, randomly shifting the usual white noise stimulus significantly improves RF estimation, and requires only short recordings.

Figure 6 illustrates our method. This paper has been published in Journal of Neurophysiology14

This figure illustrates how one estimates the receptive field of retinal ganglion cells with our approach. One presents a sequences of images composed of black and white squares of fixed size (checker board) which, in addition and in contrast to classical methods, are randomly shifted vertically and horizontally. As we show, this largely improves the resolution of the computed receptive field.

7.1.3 Retinal processing: insights from mathematical modelling

Participants: $☆$ Bruno Cessac.

Description: The retina is the entrance of the visual system. Although based on common biophysical principles the dynamics of retinal neurons is quite different from their cortical counterparts, raising  interesting problems for modellers. In this work we have addressed mathematically stated questions  in this spirit.

1. How does the structure of the retina, in particular, amacrine lateral connectivity condition the retinal response to dynamic stimuli ? With the help of a dynamical model based on the layered structure of the retina (Fig. 7) this question is addressed at two levels.
   • Level 1. Single cell response to stimuli. Using methods from dynamical systems theory, we are able to compute the receptive field of Ganglion cells resulting from the layered structure of Fig. 7. This is expressed in terms of an evolution operator whose eigenmodes characterise the response of Ganglion cells to spatio-temporal stimuli with potential mechanisms such as resonances, waves of activity induced by a propagating stimulus. We also discuss the effect of non linear rectification on the receptive field.
   • Level 2. Collective response to stimuli and spike statistics. What is the structure of the spatio-temporal correlations induced by the conjunction of the spatio-temporal stimulus and the retinal network, in particular, the amacrine lateral connectivity ? While the overlap of BCells receptive field has a tendency to correlate Ganglion cells activity, it is usually believed that the amacrine network has the effect to decorrelate these activities. We investigate this aspect mathematically and show that this decorrelation happens only under very specific hypotheses on the amacrine network. In contrast, we show how non linearities in dynamics can play a significant role in decorrelation.
2. How could spatio-temporal stimuli correlations and retinal network dynamics shape the spike train correlations at the output of the retina ? Here, we review some recent results in our group showing how the concept of spatio-temporal Gibbs distributions and linear response theory can be used to construct canonical probability distributions for spike train statistics. In this setting, one establishes a linear response for a network of interacting spiking cells, that can mimic a set of RG cells coupled via effective interactions corresponding to the A cells network influence. This linear response theory not only gives the effect of a non stationary stimulus to first order spike statistics (firing rates) but also its effect on higher order correlations.
3. We also briefly discuss some potential consequences of these results. Retinal prostheses; Convolutional networks, Implications for cortical models, Neuro-geometry.

Figure 7 illustrates the retina model used to develop our results. For more details see our paper published in J. Imaging, special issue "Mathematical Modeling of Human Vision and its Application to Image Processing", 202112.

Structure of the retina model used in the paper. A moving object moves along a trajectory. Its image is projected by the eye optics to the upper retina layers (Photoreceptors and H cells) and stimulates them. Then the layers of Bipolar (B cells), Amacrines (A Cells) and Ganglion (G cells), process this signal which is eventually converted into a spike train sent to the visual cortex.

7.1.4 The non linear dynamics of retinal waves

Participants: $☆$ Bruno Cessac, Dora Matzakou-Karvouniari${}^1$.

1 EURECOM, Sophia Antipolis, France.

Description: We investigate the dynamics of stage II retinal waves via a dynamical system model, grounded on biophysics, and analyzed with bifurcation theory. We model in detail the mutual cholinergic coupling between Starburst Amacrine Cells (SACs).

We show how the nonlinear cells coupling and bifurcation structure explain how waves start, propagate, interact and stop. We argue that the dynamics of SACs waves is essentially controlled by two parameters slowly evolving in time: one, $G_A$, controlling the excitatory cholinergic cells coupling, and the other, $G_S$, controlling cell's hyperpolarisation and refractoriness. Thanks to a thorough bifurcation diagram in the space $G_S,G_A$, we actually arrive at a quite surprising and somewhat counter intuitive result: mostly, the variety observed in waves dynamics comes from the fact that they start in a tiny region in the space $G_S,G_A$ delimited by bifurcation lines, where waves initiation is quite sensitive to perturbations such as noise. Although this region is tight, the slow dynamics returns to it in a recurrent way, regenerating the potentiality to trigger new waves sensitive to perturbations.

In addition, this scenario holds on an interval of acetylcholine coupling compatible with the variations observed in experimental studies. We derive transport equations for $G_S,G_A$, now considered as propagating fields shaping waves dynamics. From this, we are able to compute the wave speed as a function of acetylcholine coupling, as well as to show the existence of a critical value of this coupling, below which no wave can propagate. As we argue, these transport equations bare interesting analogies with the Kardar-Parisi-Zhang (KPZ) equations of interface growth on one hand, and Self-Organized Criticality on the other hand, opening up perspectives for future research. Figure 8 illustrates our results.

This paper has been published in Physica D, 11.

This figures show the bifurcation diagram of the SACs model in the plane $G_S$ (effective sAHP conductance) and $G_A$ (effective Ach conductance). There are four regions A,B,C,D corresponding to different phase portraits. In region $A$, there is a stable fixed point, corresponding to a rest state, and an unstable focus, separated by a Saddle point whose stable and unstable manifolds are shown. In region $B$, there is a unique stable fixed point, a rest state. In region $C$, there is a stable periodic orbit corresponding to fast oscillations in the voltage and activation variable . Finally, in region $D$, there is a stable fixed point Si, corresponding to a rest state, and a stable periodic orbit. These two attractors are separated by a Saddle point (Sd). Region $D$ is very narrow in the parameters space (and hardly visible on the diagram at this scale). This is nevertheless an essential zone as discussed in the paper.

Top. bifurcation diagram of the SACs model in the plane GSG_S (effective sAHP conductance) and GAG_A (effective Ach conductance). Inset: Zoom on region D. Bottom: From top to bottom, from left to right. Phase portraits of regions A,B,D,C.: This figures show the bifurcation diagram of the SACs model in the plane GSG_S (effective sAHP conductance) and GAG_A (effective Ach conductance). There are four regions A,B,C,D corresponding to different phase portraits. In region AA, there is a stable fixed point, corresponding to a rest state, and an unstable focus, separated by a Saddle point whose stable and unstable manifolds are shown. In region BB, there is a unique stable fixed point, a rest state. In region CC, there is a stable periodic orbit corresponding to fast oscillations in the voltage and activation variable . Finally, in region DD, there is a stable fixed point Si, corresponding to a rest state, and a stable periodic orbit. These two attractors are separated by a Saddle point (Sd). Region DD is very narrow in the parameters space (and hardly visible on the diagram at this scale). This is nevertheless an essential zone as discussed in the paper.

Top. bifurcation diagram of the SACs model in the plane GSG_S (effective sAHP conductance) and GAG_A (effective Ach conductance). Inset: Zoom on region D. Bottom: From top to bottom, from left to right. Phase portraits of regions A,B,D,C.: This figures show the bifurcation diagram of the SACs model in the plane GSG_S (effective sAHP conductance) and GAG_A (effective Ach conductance). There are four regions A,B,C,D corresponding to different phase portraits. In region AA, there is a stable fixed point, corresponding to a rest state, and an unstable focus, separated by a Saddle point whose stable and unstable manifolds are shown. In region BB, there is a unique stable fixed point, a rest state. In region CC, there is a stable periodic orbit corresponding to fast oscillations in the voltage and activation variable . Finally, in region DD, there is a stable fixed point Si, corresponding to a rest state, and a stable periodic orbit. These two attractors are separated by a Saddle point (Sd). Region DD is very narrow in the parameters space (and hardly visible on the diagram at this scale). This is nevertheless an essential zone as discussed in the paper.

7.1.5 A novel approach to the functional classification of retinal ganglion cells

Participants: Gerrit Hilgen${}^{1,2}$, $☆$ Evgenia Kartsaki${}^{1,5}$, Viktoriia Kartysh${}^{3,4}$, $☆$ Bruno Cessac, Evelyne Sernagor${}^1$.

1 Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK

2 Health & Life Sciences, Applied Sciences, Northumbria University, Newcastle upon Tyne UK

3 Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), 1090 Vienna, Austria

4 Research Centre for Molecular Medicine (CeMM) of the Austrian Academy of Sciences, 1090 Vienna, Austria

5 SED INRIA Sophia-Antipolis

Description: Retinal neurons come in remarkable diversity based on structure, function and genetic identity. Classifying these cells is a challenging task, requiring multimodal methodology. Here, we introduce a novel approach for retinal ganglion cell (RGC) classification, based on pharmacogenetics combined with immunohistochemistry and large-scale retinal electrophysiology. Our novel strategy allows grouping of cells sharing gene expression and understanding how these cell classes respond to basic and complex visual scenes. Our approach consists of increasing the firing level of RGCs co-expressing a certain gene (Scnn1a or Grik4) using excitatory DREADDs (Designer Receptors Exclusively Activated by Designer Drugs) and then correlate the location of these cells with post hoc immunostaining, to unequivocally characterize anatomical and functional features of these two groups. We grouped these isolated RGC responses into multiple clusters based on the similarity of their spike trains. With our approach, combined with immunohistochemistry, we were able to extend the pre-existing list of Grik4 expressing RGC types to a total of 8 and, for the first time, we provide a phenotypical description of 14 Scnn1a-expressing RGCs. The insights and methods gained here can guide not only RGC classification but neuronal classification challenges in other brain regions as well.

Figure 9 illustrates our results. This paper has been published in Royal Society Open Biology13.

This figure shows experimental results from E. Sernagor lab (performed by G. Hilgen et E. Sernagor) showing how specific genes (Grik4 and Scnn1a) are expressed in mice retina, at the level of Retinal Ganglion Cells types.

7.2.1 Constrained text generation to measure reading performance

Participants: $☆$ Alexandre Bonlarron, Aurélie Calabrèse${}^1$, $☆$ Pierre Kornprobst, Jean-Charles Régin${}^2$.

1 Aix-Marseille Université (CNRS, Laboratoire de Psychologie Cognitive, Marseille, France)

2 Université Côte d'Azur (France), I3S, Constraints and Application Lab

Description: Measuring reading performance is one of the most widely used methods in ophthalmology clinics to judge the effectiveness of treatments, surgical procedures, or rehabilitation techniques  48. However, reading tests are limited by the small number of standardized texts available. For the MNREAD test  45, which is one of the reference tests used as an example in this paper, there are only two sets of 19 sentences in French. These sentences are challenging to write because they have to respect rules of different kinds (e.g.,related to grammar, length, lexicon, and display). They are also tricky to find : out of a sample of more than three million sentences from children’s literature, only four satisfy the criteria of the MNREAD reading test. To obtain more sentences, we propose an original approach to text generation that considers all the rules at the generation stage. Our approach is based on Multi-valued Decision Diagrams (MDD). First, we represent the corpus by n-grams and the different rules by MDDs, and then we combine them using operators, notably intersections. The results obtained show that this approach is promising, even if some problems remain, such as memory consumption or a posteriori validation of the meaning of sentences. In 5-gram, we generate more than 4000 sentences that meet the MNREAD criteria and thus easily provide an extension of a 19-sentence set to the MNREAD test.

This work has been presented in the conference JFPC 2022 20 and to the Workshop MOMI 2022 26.

7.2.2 A new vessel-based method to estimate automatically the position of the non-functional fovea on altered retinography from maculopathies

Participants: Vincent Fournet, Aurélie Calabrèse${}^1$, Séverine Dours${}^{1,2}$, Frédéric Matonti${}^3$, Eric Castet${}^1$, $☆$ Pierre Kornprobst.

1 Aix-Marseille Université (CNRS, Laboratoire de Psychologie Cognitive, Marseille, France)

2 Institut d’Education Sensoriel (IES) Arc-en-Ciel

3 Centre Monticelli Paradis d'Ophtalmologie

Context: This contribution is part of a larger initiative in the scope of ANR DEVISE. We aim at measuring and analyzing the 2D geometry of each patient's "visual field," notably the characteristics of his/her scotoma (e.g., shape, location w.r.t fovea, absolute vs. relative) and gaze fixation data. This work is based on data acquired from a Nidek MP3 micro-perimeter installed at Centre Monticelli Paradis d'Ophtalmologie. In 2021, the focus was on the estimation of the fovea position from perimetric images (see below) and on the development of a first graphical user interface to manipulate MP3 data.

Description: In the presence of maculopathies, due to structural changes in the macula region, the fovea is usually located in pathological fundus images using normative anatomical measures (NAM). This simple method relies on two conditions: that images are acquired under standard testing conditions (primary head position and central fixation) and that the optic disk is visible entirely on the image. However, these two conditions are not always met in the case of maculopathies, en particulier lors de taches de fixations. Here, we propose a Vessel-Based Fovea Localization (VBFL) approach that relies on the retina's vessel structure to make predictions. The spatial relationship between fovea location and vessel characteristics is learnt from healthy fundus images and then used to predict fovea location in new images. We evaluate the VBFL method on three categories of fundus images: healthy images acquired with different head orientations and fixation locations, healthy images with simulated macular lesions and pathological images from AMD. For healthy images taken with the head tilted to the side, NAM estimation error is significantly multiplied by 4, while VBFL yields no significant increase, representing a 73% reduction in prediction error. With simulated lesions, VBFL performance decreases significantly as lesion size increases and remains better than NAM until lesion size reaches 200 deg2. For pathological images, average prediction error was 2.8 degrees, with 64% of the images yielding an error of 2.5 degrees or less. VBFL was not robust for images showing darker regions and/or incomplete representation of the optic disk. In conclusion, the vascular structure provides enough information to precisely locate the fovea in fundus images in a way that is robust to head tilt, eccentric fixation location, missing vessels and actual macular lesions.

More information is available in  41. This work was presented at ARVO 2022 28 and an extended journal version has been submitted to Translational Vision Science & Technology.

This figures illustrates how our method to detect the fovea works. It contains three images. The left image shows the vessel density map. This map is obtained by realigning and then averaging a set of vessel maps for which fovea and optic disk position are known. The are realigned based upon fixed positions of fovea and optic disk which are marked by a green cross and a green circle respectively. The middle image shows the vessel map where the fovea position is unknown. In our example, the ground truth is available allowing error estimates. Red cross indicates the true position of the fovea we are looking for. The right image shows the vessel density map in green-scale color superimposed on the vessels map after registration. The fovea position of the vessel density map after registration serves as an estimate for the fovea position. It is represented by a green cross. When ground truth is available (red cross), error can be estimated (distance between green and red crosses).

7.2.3 From Print to Online Newspapers on Small Displays: A Layouting Generation Approach Aimed at Preserving Entry Points

Participants: Sebastian Gallardo${}^1$, Dorian Mazauric${}^2$, $☆$ Pierre Kornprobst.

1 Universidad Técnica Federico Santa María, Valparaíso, Chile

2 Université Côte d'Azur (France), Inria, ABS Team

Context: The digital era transforms the newspaper industry, offering new digital user experiences for all readers. However, to be successful, newspaper designers stand before a tricky design challenge: translating the design and aesthetics from the printed edition (which remains a reference for many readers) and the functionalities from the online edition (continuous updates, responsiveness) to create the e-newspaper of the future, making a synthesis based on usability, reading comfort, engagement. In this spirit, our project aims to develop a novel inclusive digital news reading experience that will benefit all readers: you, me, and low vision people for whom newspapers are a way to be part of a well-evolved society.

Description: In this work we have focused on how to comfortably read newspapers on a small display . Simply transposing the print newspapers into digital media can not be satisfactory because they were not designed for small displays. One key feature lost is the notion of entry points that are essential for navigation. By focusing on headlines as entry points, we show how to produce alternative layouts for small displays that preserve entry points quality (readability and usability) while optimizing aesthetics and style. Our approach consists in a relayouting approach implemented via a genetic-inspired approach. We tested it on realistic newspaper pages. For the case discussed here, we obtained more than 2000 different layouts where the font was increased by a factor of two. We show that the quality of headlines is globally much better with the new layouts than with the original layout. Future work will tend to generalize this promising approach, accounting for the complexity of real newspapers, with user experience quality as the primary goal.

This work was published in the ACM Symposium on Document Engineering (DocEng ’22) 16.

(a)	(b)	(c) UW=6	(d) UW=1

7.3.1 Engineering an immersive virtual reality platform for studying embodied experiences

Participants: $☆$ Florent Robert, $☆$ Hui-Yin Wu, Lucile Sassatelli${}^{1,2,3}$, Marco Winckler ${}^{1,2,4}$.

1 Université Côte d'Azur, France

2 CNRS I3S Laboratory, France

3 Institut Universitaire de France, France

4 Centre Inria d'Université Côte d'Azur, CNRS I3S, SPARKS team, France

Context: In the context of ANR CREATTIVE3D, we are tackling the major challenge of designing 3D experiences and user tasks. This lies in bridging the inter-relational gaps of perception between the designer, the user, and the 3D scene. Paul Dourish identified three gaps of perception: ontology between the scene representation and the user and designer interpretation, intersubjectivity of task communication between designer and user, and intentionality between the user's intentions and designer's interpretations.

To address this, we developed the GUsT-3D framework for designing Guided User Tasks in embodied VR experiences, i.e., tasks that require the user to carry out a series of interactions guided by the constraints of the 3D scene. GUsT-3D is implemented as a set of tools that support a 4-step workflow to (1) annotate entities in the scene with navigation and interaction possibilities, (2) define user tasks with interactive and timing constraints, (3) manage interactions, task validation, and user logging in real-time, and (4) conduct post-scenario analysis through spatio-temporal queries using ontology definitions.

Description: We propose the GUsT-3D framework for creating 3D embodied experiences, which is comprised of three components (Figure 12):

• GUsT-3D: representing the ontology of the scene, implemented in JSON. It is used to export the annotated 3D scene, including all of the geometric properties, interactive and navigation possibilities, and their inter-relations as a spatio-temporal scene graph. It also provides the vocabulary set to define GUTasks and for user logging.
• GUTasks: representing the intersubjectivity between the designer and the user, also implemented in JSON using the same vocabulary defined in the GUsT-3D ontology. Allows the definition of user tasks to be carried out in the VR scenario, their interactive and completion constraints, and management and validation in real-time.
• Logs and query language: representing the user intentionality, implemented in LINQ 46. Allows the designer to construct spatio-temporal queries on any annotated object or user in the 3D scene.

The interactions between the user, designer, and input/output JSON files for each of the three steps of the workflow, and how they relate to the three gaps of perception.

We conducted a formative evaluation involving six expert interviews to assess the framework and the implemented workflow. Analysis of the responses show that the GUsT-3D framework fits well into a designer's creative process, providing a necessary workflow to create, manage, and understand VR embodied experiences.

The results of this work were published in the Proceedings of ACM on Human Computer Interactions (PACMHCI) and presented at the ACM Symposium on Engineering Interactive Systems (EICS)9. F. Robert presented his thesis work on the project at the doctoral consortium of the ACM Conference on Multimedia Systems (ACM MMSys)18. The technical platform has been issued a CeCILL licence (IDDN.FR.001.160035.000.S.P.2022.000.31235).

Other news and development on the project can be followed from official website.

7.3.2 On the link between emotion, attention and content in virtual immersive environments

Participants: Quentin Guimard${}^{1,2}$, $☆$ Florent Robert, Lucile Sassatelli${}^{1,2,3}$, $☆$ Hui-Yin Wu, Marco Winckler ${}^{1,2,4}$, Auriane Gros${}^5$.

1 Université Côte d'Azur, France

2 CNRS I3S Laboratory, France

3 Institut Universitaire de France, France

4 Centre Inria d'Université Côte d'Azur, CNRS I3S, SPARKS team, France

5 Université Côte d'Azur, CHU, CoBTEK, France

Context: While immersive media have been shown to generate more intense emotions, saliency information has been shown to be a key component for the assessment of their quality, owing to the various portions of the sphere (viewports) a user can attend. In this work we investigated the tri-partite connection between user attention, user emotion and visual content in immersive environments. To do so, we present a new dataset enabling the analysis of different types of saliency, both low-level and high-level, in connection with the user's state in 360${}^{\circ }$ videos. Head and gaze movements are recorded along with self-reports and continuous physiological measurements of emotions. We then study how the accuracy of saliency estimators in predicting user attention depends on user-reported and physiologically-sensed emotional perceptions. Our results show that high-level saliency better predicts user attention for higher levels of arousal. We discuss how this work serves as a first step to understand and predict user attention and intents in immersive interactive environments.

Description: This work comprises of two principal parts: the release of an open dataset on synchronized gaze and emotion for 360${}^{\circ }$ videos, and first analyses on this dataset on how emotion affects the predictability of gaze.

The PEM360 dataset contains user head movements and gaze recordings in 360${}^{\circ }$ videos, along with self-reported emotional ratings of valence and arousal, and continuous physiological measurement of electrodermal activity and heart rate. The stimuli are selected to enable the spatiotemporal analysis of the connection between content, user motion and emotion. We describe and provide a set of software tools to process the various data modalities, and introduce a joint instantaneous visualization of user attention and emotion we name “Emotional maps”. The dataset and analysis scripts are entirely made available on the PEM360 Dataset Gitlab.

Tri-partite connection between user attention, user emotion and visual content in immersive environments. Using the PEM360 dataset, we then study how the accuracy of saliency estimators in predicting user attention depends on user-reported and physiologically-sensed emotional perceptions. Our results show that high-level saliency better predicts user attention for higher levels of arousal. In Figure 13 we can visualize the level of emotional arousal as a saliency heatmap on the video.

On the still image of a roller coaster video, we see at the center a red scan path indicating a high arousal as the ride decends into a dive.

The results of this work were published to the ACM Conference on Multimedia Systems Open Dataset and Software Track (ACM MMSys) and the IEEE International Conference on Image Processing (ICIP)17, 24.

7.3.3 Evaluation of deep pose detectors for automatic analysis of film style

Participants: $☆$ Hui-Yin Wu, Luan Nguyen${}^{1,2}$, Yoldoz Tabei${}^{1,2}$, Lucile Sassatelli${}^{1,2,3}$.

1 Université Côte d'Azur, France

2 CNRS I3S Laboratory, France

3 Institut Universitaire de France, France

Context: Identifying human characters and how they are portrayed on-screen is inherently linked to how we perceive and interpret the story and artistic value of visual media. Building computational models sensible towards story will thus require a formal representation of the character. Yet this kind of data is complex and tedious to annotate on a large scale. Human pose estimation (HPE) can facilitate this task, to identify features such as position, size, and movement that can be transformed into input to machine learning models, and enable higher artistic and storytelling interpretation. However, current HPE methods operate mainly on non-professional image content, with no comprehensive evaluation of their performance on artistic film.

We thus took a first step to evaluate the performance of HPE methods on artistic film content. We begin by proposing a formal representation of the character based on cinematography theory, then sample and annotate 2700 images from three datasets with this representation, one of which we introduce to the community. An in-depth analysis is then conducted to measure the general performance of two recent HPE methods on metrics of precision and recall for character detection, and to examine the impact of cinematographic style. From these findings, we highlight the advantages of HPE for automated film analysis, and propose future directions to improve their performance on artistic film content.

Description: We propose an analysis of human pose estimators based on performance metrics associated with specific frame criteria: (1) precision, recall, and pose keypoint accuracy measures that have been used for pre-existing benchmarks, and (2) a set of cinematographic labels extending Film Editing Patterns 53 focused on character representation, and spanning six large categories: character size, character angle (both pitch and yaw), on-screen position, number of characters, body part visibility, and artistic shots.

Two HPE methods were selected: OpenPose 37 because it is a reference bottom-up approach shown to have reliable and real-time performance, and DEKR 42 because (i) it is a most recent approach shown to outperform existing competitors and because (ii) it builds on the features learned by a top-down approach, HRNet, to take the best of both bottom-up and top-down approaches. We benchmarked the performance of these two HPE methods on three datasets:

• COCO201744 composed of 106K images that are annotated with 17 joints.
• FLIC plus49 made of 17K images from film annotated with only 10 upper-body joints and ground-truth poses for up to two characters.
• TRACTIVE our new dataset of clips extracted from the corpus proposed by Brey 35. This dataset is composed of 13 clips, on which HPE methods are evaluated on a subset of frames hand-annotated for both character representation and pose estimation quality.

Six box plots showing the recall rate of the two HPE methods on the three different datasets.

Eight box plots showing the impact of artistic style on recall.

On all three datasets, we generally observe in Figure 14 that recall decreases with $N\_GT$, indicating proportionally more misses with the number of on-screen characters. In more than 25% of the cases, at least 20% of characters are missed for $N\_GT\ge 2$. For $N\_GT\ge 5$, at least 50% of characters are missed. From the results of OpenPose on the COCO and TRACTIVE datasets, we were also able to draw a number of observations on the impact of film style (Figure 15).

The results of this work were published to the Eurographics Digital Library and presented at the 10th Eurographics Workshop on Intelligent Cinematography and Editing 19.

Helping visually impaired employees to follow presentations in the company: Towards a mixed reality solution

Participants: Riham Nehmeh${}^1$, Carlos Zubiaga${}^1$, Julia-Elizabeth Luna${}^1$, Arnaud Mas${}^2$, Alain Schmid${}^3$, Christophe Hugon${}^4$, $☆$ Pierre Kornprobst.

1 InriaTech, UCA Inria, France

2 EDF, R&D PERICLES – Groupe Réalité Virtuelle et Visualisation Scientifique, France

3 EDF, Lab Paris Saclay, Département SINETICS, France

4 R&DoM

Duration: 3 months

Objective: The objective of the work is to develop a proof-of-concept (PoC) targeting a precise use-case scenario defined by EDF (contract with InriaTech, supervised by Pierre Kornprobst). The use-case is one of an employee with visual impairment willing to follow a presentation. The idea of the PoC is a vision-aid system based on a mixed-reality solution. This work aims at (1) estimating the feasibility and interest of such kind of solution and (2) identifying research questions that could be jointly addressed in a future partnership.

APP Deposit: SlidesWatchAssistant IDDN.FR.001.080024.000.S.P.2020.000.31235)

9.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program

9.2.1 Visits of international scientists

9.3.1 ANR

XR${}^2$C${}^2$

• Title:
  Extended reality research and creative center
• Programme:
  IDEX
• Duration:
  2020–
• Coordinator:
  Jean-François Trubert
• Partners:
  Involves 13 laboratories in Université Côte d'Azur
• Inria contact:
  Hui-Yin Wu, Pierre Kornprobst
• Summary:
  XR${}^2$C${}^2$ is the reference center for extended reality in the Université Côte d'Azur. It groups 13 laboratories and over 60 researchers in thematics of artistic creation, media studies, health, communications and computer science. The center is currently based in the Cannes Georges Méliès campus.

10.1.1 Scientific events: organisation

10.1.2 Scientific events: selection

10.1.3 Journal

10.1.4 Invited talks

• B. Cessac was invited at the conference "Mathematical modeling and statistical analysis in neuroscience" Paris Institut Henry Poincaré, January 31st to February 4th, 2022
• B. Cessac was invited at the conference "Workshop Stochastic Models in Life Science", Eindhoven 12-14 September, 2022.
• B. Cessac was invited at the conference "AI4Waves", Sophia-Antipolis, 10-12 October 2022.
• P. Kornprobst gave a talk entitled "La DMLA : des mécaniques d’adaptation en santé aux mécaniques ludiques" at the Workshop "Jeu vidéo, ludification, mécaniques et apprentissages", Cannes, September 2022
• Jérémy Termoz-Masson gave a talk entitled "Design et VR à destination de personnes souffrant de la Dégénérescence Maculaire Liée à l’Age" at the Workshop "Art et santé", organized by Académie 5 from UCA, Nice

10.1.5 Research administration

• B. Cessac is a member of the Bureau du Comité des Projets of the Sophia-Antipolis Inria research center.
• B. Cessac is an invited member of the Comité de Suivi et de Pilotage of the EUR Spectrum (representative of the Neuromod Institute).
• B. Cessac is a member of the board and a member of the Scientific Council of the Institute of Neurosciences and Neuromod Modeling since 2018.
• P. Kornprobst has been an elected member of the Academic Council of UCA (Conseil d’Administration) since Dec. 2019.

10.2.1 Teaching

• Master 1: B. Cessac (24 hours, cours) Introduction to Modelling in Neuroscience, master Mod4NeuCog, UCA, France.
• Master 1: Simone Ebert (15 hours, TD), Introduction to Modelling in Neuroscience, master Mod4NeuCog, UCA, France.
• License 2: J. Emonet (22 hours), Introduction à l'informatique, License SV, UCA, France.
• License 3: J. Emonet (16 hours), Programmation python et environnement linux, License BIM, UCA, France.
• License 3: J. Emonet (20 hours), Biostatistiques, License SV, UCA, France.
• Master 2: H-Y. Wu and J. Delachambre (with M. Winckler), Techniques d'interaction et multimodalité, 32 hours, Master en Informatique (SI5) mineure IHM, Polytech Nice Sophia, UCA, France
• Master 2: H-Y. Wu supervised M2 student final projects (TER), 14 hours, Master en Informatique (SI4), Polytech Nice Sophia, UCA, France
• Master 1: F. Robert (with M. Winckler), Interfaces Humaine-Machine, 38 hours, Master en Informatique (SI4), Polytech Nice Sophia, UCA, France

10.2.2 Supervision

• Master's students
  • P. Kornprobst and M. Marti (UCA, LIRCES) co-supervised Quentin Garnier, Ludivine Martin, and Léo Riba on "La Forêt de Brocéliande : Conception d’un jeu adapté aux patients souffrant de dégénérescence maculaire liée à l’âge", Master 1 and 2, Mention Humanités et Industries Créatives Parcours Management, Jeux Vidéo, Image et Créativité (March - September 2022).
  • P. Kornprobst and H-Y. Wu co-supervised Johanna Delachambre on an apprenticeship for engineering virtual reality systems
  • H-Y. Wu supervised Clément Merveille on "Analysis and visualization of eye tracking data in virtual reality under normal and simulated low-vision conditions" (April - September 2022)
  • H-Y. and L. Sassatelli co-supervised Geneviève Masioni on "Image and text processing for multimodal film analysis" (March - September 2022)
  • H-Y. Wu and L. Sassatelli co-supervised Clément Quéré on "Modeling 6 DoF human motion data in extended reality" (March - August 2022)
• PhD students
  • PhD defended: Evgenia Kartsaki, “How Specific Classes of Retinal Cells Contribute to Vision: a Computational Model”, October 2017–March 2022, co-supervised by B. Cessac and E. Sernagor (Newcastle).
  • PhD in progress: Simone Ebert on “Dynamical Synapse in the Retinal Network”, started in October 202, supervised by B. Cessac. Funding UCA Institut Neuromod.
  • PhD in progress: Jérome Emonet, "A computational model of the retino-thalamo-cortical pathway", started in October 2021, supervised by B. Cessac in collaboration with A. Destexhe. Funding ANR ShootingStar.
  • PhD in progress: Alexandre Bonlarron. "Pushing the limits of reading performance screening with Artificial Intelligence: Towards large-scale evaluation protocols for the Visually Impaired", started in October 2021, co-supervised by P. Kornprobst, A. Calabrèse and J.-C. Régin.
  • PhD in progress: Florent Robert, "Analyzing and Understanding Embodied Interactions in Extended Reality Systems", started in October 2021, supervised by H-Y. Wu, M. Winckler and L. Sassatelli.
  • PhD in progress: Johanna Delachambre. "Social interactions in low vision: A collaborative approach based on immersive technologies", started in November 2020, co-supervised by P. Kornprobst and H.-Y. Wu.
  • PhD in progress: Franz Franco Gallo, "Modeling 6DoF Navigation and the Impact of Low Vision in Immersive VR Contexts", started in November 2022, supervised by H-Y. Wu and L. Sassatelli.
  • PhD in progress: Julie Tores, "Deep Learning to detect objectification in films and visual media", started in November 2022, supervised by L. Sassatelli, F. Précioso and H-Y. Wu

10.2.3 Juries

• B. Cessac was a member of the jury CRCN (Chargé de Recherche) INRIA, 8-10/06.
• B. Cessac was a member of the jury for the defense of Nicholas Gale's PhD, "Theoretical Investigations into Principles of Topographic Map Formation and Applications ", Cambridge University, May 6th.
• B. Cessac was a member of the jury for the defense of Jhunlyn Lorenzo's PhD, "Modelisation de linteraction neurone-astrocyte: application au traitement du signal et des images ", Université de Dijon, September 27th.
• P. Kornprobst has been a member of the Comité de Suivi de Individuel (CSI) of Victor Jung, PhD candidate at I3S, working on "Apprentissage de contraintes cachées" and supervised by Jean-Charles Régin.
• P. Kornprobst has been a member of the Comité de Suivi de Individuel (CSI) of Abid Ali, PhD candidate at Inria, Stars project-team, working on "Action detection for improving the diagnosis of autism" and co-supervised by François Brémond and Susanne Thümmler.
• H.-Y. Wu was a member of the jury for the defense of Alexandre Bruckert, Univ. Rennes 1, Inria MimeTIC team, for his thesis on “A Perceptual-driven approach to film editing”, March 2022.
• H.-Y. Wu was a member of the jury for the defense of Ludovic Burg, Univ. Rennes 1, Inria Virtuos team, for his thesis on “Real-time Virtual Cinematography for Target Tracking”, November 2022.

10.3.1 Interventions

• B. Cessac gave a public conference on "Vision, rétine et mathématiques", Rotary Club, Grasse, 02/06
• H.-Y. Wu presented her research to the ABC International School as part of Chiche!

International journals

• 11 articleB.Bruno Cessac and D.Dora Matzakou-Karvouniari. The non linear dynamics of retinal waves.Physica D: Nonlinear PhenomenaNovember 2022
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• 12 articleB.Bruno Cessac. Retinal processing: insights from mathematical modelling.Journal of Imaging81January 2022, 14
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• 13 articleG.Gerrit Hilgen, E.Evgenia Kartsaki, V.Viktoriia Kartysh, B.Bruno Cessac and E.Evelyne Sernagor. A novel approach to the functional classification of retinal ganglion cells.Open Biology12210367March 2022
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• 14 articleD.Daniela Pamplona, G.Gerrit Hilgen, M. H.Matthias H Hennig, B.Bruno Cessac, E.Evelyne Sernagor and P.Pierre Kornprobst. Receptive field estimation in large visual neuron assemblies using a super-resolution approach.Journal of Neurophysiology12752022, 1334--1347
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• 15 articleH.-Y.Hui-Yin Wu, A.Aurélie Calabrèse and P.Pierre Kornprobst. An Open Virtual Reality Toolbox for Accessible News Reading.ERCIM News130July 2022
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International peer-reviewed conferences

• 16 inproceedingsS.Sebastián Gallardo Dïaz, D.Dorian Mazauric and P.Pierre Kornprobst. From Print to Online Newspapers on Small Displays: A Layout Generation Approach Aimed at Preserving Entry Points.ACM Symposium on Document Engineering (DocEng ’22)San Jose, United StatesSeptember 2022
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• 17 inproceedingsQ.Quentin Guimard, F.Florent Robert, C.Camille Bauce, A.Aldric Ducreux, L.Lucile Sassatelli, H.-Y.Hui-Yin Wu, M.Marco Winckler and A.Auriane Gros. PEM360: A dataset of 360° videos with continuous Physiological measurements, subjective Emotional ratings and Motion traces.MMSys 2022 - 13th ACM Multimedia Systems ConferenceAthlone, IrelandJune 2022
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• 18 inproceedingsF. A.Florent Alain Sauveur Robert, M.Marco Winckler, L.Lucile Sassatelli and H.-Y.Hui-Yin Wu. Research Proposal: Analyzing and Understanding Embodied Interactions in Virtual Reality Systems.ACM MMSys 2022 - 13th ACM Multimedia Systems ConferenceAthlone, IrelandJune 2022
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• 19 inproceedingsH.-Y.Hui-Yin Wu, L.Luan Nguyen, Y.Yoldoz Tabei and L.Lucile Sassatelli. Evaluation of deep pose detectors for automatic analysis of film style.10th Eurographics Workshop on Intelligent Cinematography and EditingReims, FranceAssociation for Computing Machinery (ACM)April 2022
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National peer-reviewed Conferences

• 20 inproceedingsA.Alexandre Bonlarron, A.Aurelie Calabrese, P.Pierre Kornprobst and J.-C.Jean-Charles Régin. Constrained text generation to measure reading performance: A new approach based on multivalued decision diagrams.JFPC 2022 - Journées Francophones de Programmation par ContraintesSaint-Étienne, FranceJune 2022
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Conferences without proceedings

• 21 inproceedingsE.Eric Castet, J.Jérémy Termoz-Masson, J.Johanna Delachambre, C.Christophe Hugon, H.-Y.Hui-Yin Wu and P.Pierre Kornprobst. PTVR : a user-friendly open-source script programming package to create Virtual Reality experiments.ECVP 2022 - European Conference on Visual PerceptionNijmegen, NetherlandsAugust 2022
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• 22 inproceedingsB.Bruno Cessac. Retinal processing: Insights from mathematical modelling.EURANDOM workshop on Stochastic Models in Life SciencesEindhoven, NetherlandsSeptember 2022
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• 23 inproceedingsB.Bruno Cessac. Waves4AI.AI4WavesSophia-Antipolis, FranceOctober 2022
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• 24 inproceedingsQ.Quentin Guimard, F.Florent Robert, C.Camille Bauce, A.Aldric Ducreux, L.Lucile Sassatelli, H.-Y.Hui-Yin Wu, M.Marco Winckler and A.Auriane Gros. On the link between emotion, attention and content in virtual immersive environments.ICIP 2022 - IEEE International Conference on Image ProcessingBordeaux, FranceOctober 2022
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Doctoral dissertations and habilitation theses

• 25 thesisE.Evgenia Kartsaki. How specific classes of retinal cells contribute to vision : a computational model.Université Côte d'Azur; Newcastle University (Newcastle upon Tyne, Royaume-Uni)March 2022
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Other scientific publications

• 26 inproceedingsA.Alexandre Bonlarron, A.Aurelie Calabrese, P.Pierre Kornprobst and J.-C.Jean-Charles Régin. Génération de texte sous contraintes pour mesurer des performances de lecture : Une nouvelle approche basée sur les diagrammes de décisions multivalués.MOMI2022 - Le Monde des Mathématiques IndustriellesBiot, FranceMay 2022
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• 27 inproceedingsT.Thomas Buffet, S.Semihchan Sermet, O.Olivier Marre, B.Bruno Cessac and S.Simone Bertoli. Dynamical Synapses in Retinal Surprise coding.ICMNS 2022 - 2022 International Conference on Mathematical Neuroscienceonline, FranceJuly 2022
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• 28 inproceedingsA.Aurelie Calabrese, V.Vincent Fournet, S.Severine Dours, F.Frédéric Matonti, E.Eric Castet and P.Pierre Kornprobst. A new vessel-based method to estimate automatically the position of the non-functional fovea on altered retinography from maculopathies.ARVO 2022 - Annual Meeting Association for Research in Vision and OphthalmologyDenver, United StatesMay 2022
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• 29 miscB.Bruno Cessac. The non linear dynamics of retinal waves.Paris, FranceJanuary 2022
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• 30 inproceedingsS.Simone Ebert, T.Thomas Buffet, S.Semichan Sermet, O.Olivier Marre and B.Bruno Cessac. The Role of Dynamical Synapses in Retinal Surprise Coding.Dendrites 2022 - Dentritic, anatomy, molecules and functions - Embo workshopHeraklion, GreeceMay 2022
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• 31 inproceedingsS.Simone Ebert, T.Thomas Buffet, S.Semichan Sermet, O.Olivier Marre and B.Bruno Cessac. The Role of Dynamical Synapses in Retinal Surprise Coding.AREADNE 2022 Conference : Research in Encoding And Decoding of Neural EnsemblesFira, GreeceJune 2022
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• 32 inproceedingsS.Simone Ebert, T.Thomas Buffet, S.Semichan Sermet, O.Olivier Marre and B.Bruno Cessac. The Role of Dynamical Synapses in Retinal Surprise Coding.NEUROMOD Meeting 2022Antibes, FranceJune 2022
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