Thèse

Current models of visual perception agree that vision is a proactive process. This implies that visual perception consists in constantly matching the characteristics of a visual input (e.g., two bright dots on a misty road) to expectations, built on past experiences and learnt regularities in the environment (e.g., high probability to encounter a car in the opposite direction). Such proactive processes would therefore facilitate the processing of frequently encountered stimuli. Indeed, past studies have shown that an expected stimulus (e.g., a soccer player on a soccer field) is categorized more quickly than unexpected ones (e.g., a soccer player in a kitchen). They would also be particularly useful in cases where the visual input is noisy or ambiguous and an analysis purely based on it would be inefficient (e.g., in the example above, the analysis of two bright dots would not enable to recognize the stimulus as a car, without prior expectations). Neurobiologically, this “predictive coding” of visual information has been modelled as the permanent interaction between different levels of the processing hierarchy of prediction (i.e., expected features of the input) and prediction error signals (i.e., unexpected features allowing to update expectations), which relative weights would vary according to visual constraints (e.g., prediction signals would weight more when the visual input is noisy while prediction error signals would weight more when the stimulus is unambiguous and expectations are invalid). However, how expectations modulate the processing of visual information and influence perception remains debated. For example, it has been suggested that prediction signals facilitate the processing of expected visual features by increasing the sensitivity of neurons tuned to these features, which would then be perceived as sharper. On the other hand, unexpected stimuli would result in increased prediction error signals, and thus increased activity in neuron populations tuned to unexpected features, leading to a more intense percept of these features. Recent experimental data support these assumptions but they have never been systematically tested.
The aim of the present PhD project is therefore to precise the mechanisms by which prior knowledge and expectations do influence visual perception, using perceptual judgment tasks on various stimuli characteristics (e.g., sharpness, vividness…) and according to different factors such as the availability and/or validity of expectations and the reliability of the visual inputs in behavioral studies. These studies will be performed in healthy individuals and may be followed by eye-tracking studies, in order to examine how expectations influence visual attention, as well as electroencephalography studies in order to assess the time course of these mechanisms at the cerebral level.

Encadrantes :
- Carole PEYRIN -  carole.peyrinuniv-grenoble-alpes.fr (carole[dot]peyrin[at]univ-grenoble-alpes[dot]fr)
- Louise KAUFFMANN - louise.kauffmanngmail.com (louise[dot]kauffmann[at]gmail[dot]com)

Keywords : électroencéphalographie,codage prédictif,perception visuelle

• Read more about Thesis Pauline ROSSEL

The human brain continuously receives new information from external stimuli. Information received from each senses is collected, analyzed and combined with those of other senses (vision, hearing, touch etc…) in order to be interpreted. Each new information does not overwrite with previously learnt ones but comes to extend the brain knowledge.
Artificial intelligence deep learning algorithms aims to simulate this type of learning. Nevertheless, for now, computers can have many sensors that receive external information but they do not necessarily communicate with each other to share and “understand” the global information. Furthermore, when deep learning algorithm learns new knowledge, it overlaps them with old ones and most of the time, old knowledge are forgotten. We name this type of forgetfulness, catastrophic forgetting. Behind these observations, we find one of the major challenge of tomorrow’ deep learning systems: How intelligent system could adapt in a changing environment? Could robot be adaptable to everyone?
To answer those questions, researchers introduced the notion of incremental learning, personalization and multimodality that are three growing research fields in a global field of deep learning called life-long learning.
An incremental learning algorithm is currently developed in our research laboratory. Results obtained with it are already encouraging for datasets like MNIST, CIFAR10 and CIFAR100 (Solinas et al.). This type of algorithm enables to overcome catastrophic forgetting of previously learnt classes. Some researchers proposed to use the advantage of incremental learning for the learning of new instances of known classes (Lomonaco and Maltoni). This type of use of incremental learning could give the possibility to personalize its algorithm to new unknown instances. In parallel, an interesting paper explored the learning of two modalities in a spiking neural network: audio and image for MNIST dataset (Rathi and Roy 2019) and shows that multimodality as the advantage to improve accuracy and to be more robust to noisy data.
We would like to position our thesis project in the heart of these researches and propose a framework that answers the burning deep learning issue of an incremental multimodal learning which can be also adapted to personalization question.

Supervisors
- Martial MERMILLOD - martial.mermilloduniv-grenoble-alpes.fr (martial[dot]mermillod[at]univ-grenoble-alpes[dot]fr)
- Marina REYBOZ -  marina.reybozcea.fr (marina[dot]reyboz[at]cea[dot]fr) -
- Christelle GODIN -  christelle.godincea.fr (christelle[dot]godin[at]cea[dot]fr)

Keywords : Continual Learning,Incremental learning,Emotion detection,Multimodal Fusion,Deep Learning,Personalization,

• Read more about Thesis Marion MAINSANT

What mechanism determines access to perceptual awareness? In this thesis, I propose to test the following hypotheses: (1) perceptual awareness is determined by the accumulation of evidence and (2) this accumulation process, when it reaches a threshold, gives rise to the signature of awareness postulated by the 'global workspace', a theory of consciousness. Behavioural and electroencephalographic data will be compared with predictions made by a computational model in order to test the validity of the first hypothesis. In addition, intracranial electrophysiological data will be collected in humans in order to determine which precise regions are involved in this accumulation process and to test the validity of the second hypothesis.

Supervisors :
- Nathan FAIVRE - 0652937884 - nathan.faivreuniv-grenoble-alpes.fr (nathan[dot]faivre[at]univ-grenoble-alpes[dot]fr)
- Michael PEREIRA - +41 79 409 9746 - Michael.pereirauniv-grenoble-alpes.fr (Michael[dot]pereira[at]univ-grenoble-alpes[dot]fr) -

Keywords : Metacognition, Conscience, sEEG

• Read more about Thesis François STOCKART

In this PhD project, we are interested in pathophysiological alterations of consciousness flow that are brutally interfering with the patient’s interactions with the world. Moreover, because of their critical implications for consciousness flow, we are interested in the neurocognitive dynamism constituting attentional modulations. To do so, we are working with a neurological, pediatric population suffering from childhood absence epilepsy (CAE). This specific population presents with abrupt and transitive alterations of consciousness and attentional deficits. CAE is classified in the group of idiopathic generalized epilepsy. The actual dominant hypothesis proposes a neurodevelopmental origin to both ictal and interictal deficits associated with CAE. This epilepsy is characterized by absence seizures, which are defined by bilateral generalized spike-wave discharges associated with clinical alteration of consciousness. Attentional difficulties are very well known in this epilepsy, considering structural, functional, and behavioral studies from the literature. This work aims to identify cerebral and cognitive processes involved in the constitution of consciousness flow by studying attentional investment and disinvestment through the pathological model of epilepsy. On the one hand, childhood absence epilepsy is considered the gold standard in the study of neurophenomenology. On the other hand, this specific population allows us to study the fundamental links underlying pathological alterations of consciousness and attentional network deficits.

From a fundamental perspective, it is crucial to improve both theoretical and empirical knowledge to define consciousness flow by studying attentional dynamism. Moreover, we would like to extend our model to the grand diversity of ictal and interictal generalized spike wave discharges. From a clinical perspective, it is necessary to improve biomedical knowledge about absence seizure initiation mechanisms because absence seizures aren’t systematically controlled by antiepileptic medication and are highly impacting patients’ life. From a long-term perspective, it is essential to propose alternative care for absence seizures, for example, by using cognitive remediation to reduce absence seizure occurrences.

Supervisors :
Laurent VERCUEIL LVercueilchu-grenoble.fr (LVercueil[at]chu-grenoble[dot]fr)
Juan VIDAL jvidaluniv-catholyon.fr (jvidal[at]univ-catholyon[dot]fr) (Co-encadrant)

Keywords : attentional investment,attentional disinvestment,Consciousness flow,Absence epilepsy,Blip syndrome,EEG / iEEG,

• Read more about Thesis Edgar MATRINGE

Preserving the quality of life of the elderly, or even dependent persons, is a major public health issue in view of the ageing of the population. Indeed, physical ageing is often associated with neurocognitive ageing that modifies functions such as memory, attention, executive functions, motor functions and emotions. This is particularly disabling in the context of a neurodegenerative pathology such as Alzheimer's (temporo-spatial disorientation, specific language disorders, psycho-behavioral disorders). Taking care of people in the early stages of the disease means intervening on the first difficulties that arise and developing compensation strategies before the irreparable loss of autonomy. This is now possible by using multidisciplinary approaches combining a wide range of fields: psychology, neuroscience, computer science and digital technology.
This project has a significant applicative aim: its outcome should allow the commercialization of an intelligent digital tablet, 'Lily', adapted to people suffering from neurodegenerative pathologies. It will include organizational functions relevant to memory disorders (e.g. diary, reminder), communication functions (e.g. videoconferencing, messaging), and also cognitive stimulation functions to maintain a certain quality of life for a prolonged period.
For the cognitive stimulation, our project seeks to explore the motivational tools used in traditional video games in order to apply and adapt them to a specific public suffering from cognitive disorders, where the loss of motivation, the disengagement and the lack of therapeutic adherence is not in favor of the efficiency of the already existing remediations. In parallel, we seek to implement methods of human-machine interaction based on concepts of embodied cognition to anchor in memory key information for the preservation of the quality of life, as much as possible at home. This is achieved by involving emotions and actions as positive factors that reinforce memory processes. Our objective is to maintain traces that are subject to forgetting, and even to build new ones.

Supervisors
- Céline BORG - celine.borgchu-st-etienne.fr (celine[dot]borg[at]chu-st-etienne[dot]fr)
- Pascal HOT - pascal.hotuniv-smb.fr (pascal[dot]hot[at]univ-smb[dot]fr)
- Dorothée FURNON -  d.furnonlink-ia.com (d[dot]furnon[at]link-ia[dot]com)

Keywords : embodiment,action,memory,emotion,alzheimer disease,

Financement

Collaboration Industrielle établie:

Société : Linkia SAS
Correspondant : Dorothée Furnon d.furnonlink-ia.com (d[dot]furnon[at]link-ia[dot]com)

 

CONVENTION CIFRE

• Read more about Thesis David BOUVAREL

Eye movements constitute a reliable marker of some aspects of cerebral processes. For example, the time needed to initiate an eye movement toward a face can inform about the speed of face recognition while the ability to voluntarily suppress a reflexive eye movement toward a flashed point can be used to assess the integrity of inhibition processes. The investigation of eye movements, using eye-tracking methods, appears as an attractive tool to easily and non-invasively assess the normal cerebral functioning.
In this context, the aim of the PhD project is to (1) record eye movements in a large sample of healthy participants of different age range using three visual oculomotor tasks, (2) to extract and analyze several eye movement parameters to asses different cognitive, visual and motor processes in order to identify a multidimensional signatures of normal cerebral functioning, and (3) to measure the impact of normal ageing on this signature.
This PhD project should have significant impact to develop new diagnosis and/or mental and cerebral health screening tools based on eye-tracking.

Supervisors :
- Anne GUERIN-DUGUE  anne.gueringipsa-lab.grenoble-inp.fr (anne[dot]guerin[at]gipsa-lab[dot]grenoble-inp[dot]fr)
- Nathalie GUYADER - nathalie.guyadergipsa-lab.grenoble-inp.fr (nathalie[dot]guyader[at]gipsa-lab[dot]grenoble-inp[dot]fr)
- Louise KAUFFMANN - louise.kauffmanngmail.com (louise[dot]kauffmann[at]gmail[dot]com)

Keywords : eye movements,signal processing,model,

• Read more about Thesis Chuyao WANG

The massive proliferation of DL methods in all fields of application has raised awareness of the two limitations of these very powerful methods for statistical data processing: the first is the exorbitant cost (both energy and financial) of training neural networks, and the second is the dependence on access to massive quantities of data and, above all, their annotations.
One way of getting around these limitations is to train the network as close as possible to the sensors, in order to limit the need for massive data movements and the associated infrastructure costs, as well as continuous learning to respond to data that may not all be available initially. The Edge computing trend is now attempting to bring computing closer to sensors by integrating AI into low-power electronic devices. This approach is already well studied, particularly in terms of efficient neuromorphic architectures, but it still only concerns the inference phase.
In this thesis project, we focus on the learning phase by studying the on-line learning capabilities of artificial neural networks. Although our ultimate aim is to develop techniques that benefit from the advantages of these neuromorphic architectures, the first phase of this study will consist of exploiting formal neural networks (DL) in order to create a basis for comparison in terms of the accuracy of our models, their computing cost, their data requirements and their energy consumption. By collecting data from different (multimodal) sensors, these intelligent systems can detect changes in the data and the environment that generates them. These changes may require the underlying neural network to learn incrementally, either to adjust its model to new conditions, or to learn new categories depending on the problem being addressed. In all cases, this relearning must be carried out in a reduced time and energy budget.
The problem of on-line learning has to contend with a number of obstacles that are still not the subject of consensus in the literature: novelty detection, learning with few or no labels, and catastrophic forgetting in the face of continuous learning of new data.
Yet the biological brain naturally manages the constant changes in our environment from a very early age right through to adulthood. It has a wide range of plasticity capacities, which are revealed at several levels of its organisation. In particular, it exploits the spatio-temporal correlations arising from the different sensory modalities it uses to apprehend its environment. These modalities merge and complement each other while being processed and routed by different neural pathways.
In this project, we therefore want to study how projection between modalities can help to improve the quality of lifelong learning, by overcoming the problem of catastrophic forgetting while reducing the need for data annotation.

Supervisors :
BENOIT MIRAMOND benoit.miramonduniv-cotedazur.fr (benoit[dot]miramond[at]univ-cotedazur[dot]fr)
Marina REYBOZ marina.reybozcea.fr (marina[dot]reyboz[at]cea[dot]fr) (Codirection)
Martial MERMILLOD martial.mermilloduniv-grenoble-alpes.fr (martial[dot]mermillod[at]univ-grenoble-alpes[dot]fr) (Codirection)

Keywords :
Incremental learning, Multimodal learning, Neuromorphic architectures, Embedded artificial intelligence

• Read more about Thesis Jonathan GRIENAY

Psychogenic non-epileptic seizures (PNES) constitute the most common subtype of functional neurological disorder (FND). The underlying pathophysiology remains unexplained and specific therapeutic tools are not present contributing to a poor prognosis and major healthcare costs. Behavioral and neuroimaging studies suggest disrupted emotional processes in PNES. However, the current data remains limited and inconsistent due to varying research methods. Therefore, PNES have been repeatedly associated with disrupted autonomic nervous system (ANS) patterns. The ANS is of particular interest because of its involvement in emotional processes. According to models, effective emotional regulation depends on a coupling between cognitive and autonomic responses. The hypothesis of a relationship between autonomic profiles, brain activity and emotional disorders in patients with PNES needs to be elucidated. An original approach to test whether PNES are linked to dysautonomic responses, consists of assessing how artificial modulation of ANS can modulate both emotional regulation and PNES. The biofeedback (BFB) is a non-invasive and efficient approach based on a neuromodulation of autonomic control. This technique has been emphasized as a potential therapeutic tool in epileptic disorders, but it has never been assessed in adult PNES. It is a promising tool for understanding the causes of emotional disorders and for developing a functional therapy for PNES.
The purpose of this project is to develop a personalized approach and an evidence-based therapeutic management, and aims to: (1) Explore the neurobiological substrates underlying emotional disorders in PNES, (2) Search for autonomic endophenotypes in patients, (3) Determine whether BFB is a beneficial therapeutic approach for the improvement of PNES, and (4) Develop a computational model of PNES. We hypothesize some PNES patients have a dysautonomic profile with a defect into the autonomic and emotional coupling, which may be improved by BFB, and this effect will be determined by a multimodal investigation and predicted by a computational analysis.
This multidisciplinary project will be part of a co-directed thesis initiating a new international collaboration between two Psychology and Neurocognition (Pr. Hot, Chambéry) and Neurology (Pr. Nguyen, Montréal, Canada) teams. Four sequential phases will be developed using an innovative experimental protocol. The first phase will investigate the emotional specificities in PNES. Based on a G power calculation, 21 subjects in 3 comparative groups each such as PNES, healthy controls and epileptic patients will be included. All subjects will perform emotional induction (T1) and re-exposure (T2) tasks combined with multimodal analyses including behaviour (B), psychophysiology (P) (heart rate variability (HRV) and skin conductance activity), and neuroimaging (N). Phase 2 will examine the therapeutic effect of HRV-BFB on PNES. Patients with PNES will carry out consecutive randomized BFB or pseudo BFB protocols for 6 weeks. Each patients will be their own control. The effects of HRV-BFB on change in seizure frequency and quality of life will be calculated. In Phase 3, patients will perform a final emotional re-exposure (T3), and B, P, and N measures will be repeated. According to the findings from Phase 1 and 2, the impact of BFB on emotional reactivity and potential changes in neurobiological patterns will be examined. In Phase 4, a computational model derived from the previous experimental data will be developed. This model will encode the influence of ANS on the occurrence of PNES, and assess the predictive value of different endophenotypes in the response to BFB. Given the major challenge posed by PNES, it seems essential to understand the underlying pathophysiology and to target it to develop an integrative personalized therapy. Finally, the large database and the computational model developed will enable further shared international collaborations.

Supervisors :
Pascal HOT - PR USMB - pascal.hotuniv-smb.fr (pascal[dot]hot[at]univ-smb[dot]fr)
Dang Khoa Nguyen - d.nguyenumontreal.ca (d[dot]nguyen[at]umontreal[dot]ca)

Keywords : Psychogenic nonepileptic seizures,Emotional regulation,Autonomic nervous system,Multimodal imaging,Biofeedback,Computational Neurosciences

• Read more about Thesis Jasmine CARLIER

Unhealthy lifestyles are major factors contributing to chronic conditions that impose a huge financial burden in EU healthcare systems. Poor diet is a significant risk factor for cancers, cardiovascular, chest, metabolic disorders and is a leading cause of morbidity and premature mortality. Unfortunately, communication of Public Health has failed to influence consumers to change their habits. This could be explained by the fact that there is a lack of awareness of the contextual features influencing eating behaviour and even where there is motivation to change, people have difficulty translating good intentions into healthy behaviours. In this project we will focus on the obesity ‘epidemic’.
Our proposal is that advances in the field of metacognition could bring to bear on complex changes in eating behaviours. Metacognition has been defined as ‘thinking about thinking’ and broadly speaking it refers to a system of conscious awareness which regulates our behaviours according to the current state of the organism and its intended goals. It has been studied using a variety of methods ranging from the social sciences to the neurosciences, and it is of use both in tackling applied issues and the exploration of conscious awareness. In eating behaviours, a metacognitive approach will reveal scientifically for the first time whether people are consciously able to access the nature and quantity of what they are eating. In short, we ask first whether a metacognitive failure might be the cause of over-eating (or eating the wrong thing). Second, we will explore what the metacognitive approach might be able to contribute to healthier eating. Our unique hypothesis is that complex decisions about what and when and how much to eat can be better understood by adopting a metacognitive viewpoint; something which has not yet been considered in human nutrition.
In a new collaboration on the Grenoble UGA site drawing on resources in the SFR Nutrition, we will use a funded PhD student to explore metacognition and eating behaviours in 3 pre- registered well-well-powered experiments and one naturalistic, on-line diary study. This project will be the launching point of larger scale multi-site and multidisciplinary projects. In the supervised PhD thesis, we will draw on multi-method approaches to measure eating behaviours and the awareness of them, from a psychological, neuroscientific and - thanks to input from the UFR nutrition - biological systems viewpoint.
In sum, if people are detached from or unaware of what they are eating, it will lead to dysfunctional eating. This would explain also why self-report measures of eating are so poor at predicting behaviour-change and real-world behaviours: people simply aren’t aware of their food choices and behaviours. Implementing a metacognitive approach allows the study of - and perhaps the elimination of - so-called ‘mindless eating'.

Supervisors :
- Christopher MOULIN - christopher.moulinuniv-grenoble-alpes.fr (christopher[dot]moulin[at]univ-grenoble-alpes[dot]fr)
- Eve DUPIERRIX - eve.dupierrixuniv-grenoble-alpes.fr (eve[dot]dupierrix[at]univ-grenoble-alpes[dot]fr) -
- Christophe MOINARD - christophe.moinarduniv-grenoble-alpes.fr (christophe[dot]moinard[at]univ-grenoble-alpes[dot]fr)

Keywords : metacognition,obesity,eating behaviours,

• Read more about Thesis Milèna LEGER

A critical issue in today’s ageing society is how to better understand and mitigate for cognitive changes. The central topic of this proposed research is metamemory: the ability to reflect upon and monitor our memory. Understanding metamemory would help us understand the concept of ‘cognitive reserve’: protective factors in the maintenance of cognitive function in older adults. First we need to know which forms of metamemory are altered in ageing. We will discover the status of metacognitive accuracy in several tasks using hierarchical Bayesian modelling. We will compare the impact of age and examine the relationship between metamemory with well-being, emotion, quality of life and cognitive reserve. We will establish a national model ‘Seniors for Science’ in collaboration with the Maison de Science de l’Homme – enabling senior French citizens to contribute to research programmes and learn about their memory function. We will rise to meet the challenges of open, reproducible science.

Supervisors :
- Christopher MOULIN - christopher.moulinuniv-grenoble-alpes.fr (christopher[dot]moulin[at]univ-grenoble-alpes[dot]fr)
- Céline SOUCHAY - celine.souchayuniv-grenoble-alpes.fr (celine[dot]souchay[at]univ-grenoble-alpes[dot]fr)

Keywords :FOK,Memory,metacognition,

• Read more about Thesis Lucile MEUNIER