Papers by Frederic Alexandre
Recently, models in Computational Cognitive Neuroscience (CCN) have gained a renewed interest bec... more Recently, models in Computational Cognitive Neuroscience (CCN) have gained a renewed interest because they could help analyze current limitations in Artificial Intelligence (AI) and propose operational ways to address them. These limitations are related to difficulties in giving a semantic grounding to manipulated concepts, in coping with high dimensionality and in managing uncertainty. In this paper, we describe the main principles and mechanisms of these models and explain that they can be directly transferred to Computational Creativity (CC), to propose operational mechanisms but also a better understanding of what creativity is.
Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015
Neuronal models of associative memories are recurrent networks able to learn quickly patterns as ... more Neuronal models of associative memories are recurrent networks able to learn quickly patterns as stable states of the network. Their main acknowledged weakness is related to catastrophic interference when too many or too close examples are stored. Based on biological data we have recently proposed a model resistant to some kinds of interferences related to heteroassociative learning. In this paper we report numerical experiments that highlight this robustness and demonstrate very good performances of memorization. We also discuss convergence of interests for such an adaptive mechanism for biological modeling and information processing in the domain of machine learning.
Cognitive Computation manuscript No. (will be inserted by the editor) A dynamic neural field appr... more Cognitive Computation manuscript No. (will be inserted by the editor) A dynamic neural field approach to the covert and overt deployment of spatial attention
2017 International Joint Conference on Neural Networks (IJCNN), 2017
The neurocomputational model described here proposes that two dimensions involved in computation ... more The neurocomputational model described here proposes that two dimensions involved in computation of reward prediction errors i.e magnitude and time could be computed separately and later combined unlike traditional reinforcement learning models. The model is built on biological evidences and is able to reproduce various aspects of classical conditioning, namely, the progressive cancellation of the predicted reward, the predictive firing from conditioned stimuli, and delineation of early rewards by showing firing for sooner early rewards and not for early rewards that occur with a longer latency in accordance with biological data.
Proceedings of the 8th International Joint Conference on Computational Intelligence, 2016
Recently, Machine Learning has achieved impressive results, surpassing human performances, but th... more Recently, Machine Learning has achieved impressive results, surpassing human performances, but these powerful algorithms are still unable to define their goals by themselves or to adapt when the task changes. In short, they are not autonomous. In this paper, we explain why autonomy is an important criterion for really powerful learning algorithms. We propose a number of characteristics that make humans more autonomous than machines when they learn. Humans have a system of memories where one memory can compensate or train another memory if needed. They are able to detect uncertainties and adapt accordingly. They are able to define their goals by themselves, from internal and external cues and are capable of self-evaluation to adapt their learning behavior. We also suggest that introducing these characteristics in the domain of Machine Learning is a critical challenge for future intelligent systems.
We share a new exploratory action known as Artificial Intelligence Devoted to Education (AIDE) la... more We share a new exploratory action known as Artificial Intelligence Devoted to Education (AIDE) launched with the support of Inria (Mnemosyne Team) and Nice INSPE from Cote d´Azur University (LINE laboratory) in connection with the Bordeaux NeuroCampus. It positions artificial intelligence in a somewhat original way ... not [only] as a disruptive tool, but as a formalism allowing to model learning human in problem-solving activities.
Amygdala and hippocampus are key structures in pavlovian conditioning. From existing models of th... more Amygdala and hippocampus are key structures in pavlovian conditioning. From existing models of these cerebral structures, we test them on corpus, to assess their capacities and also their performances in critical situation. From these results, we propose adaptations to these models together with an early study on forgetting.
Le recours a la modelisation et a la simulation permet aujourd'hui des performances considera... more Le recours a la modelisation et a la simulation permet aujourd'hui des performances considerables pour les previsions meteorologiques ou pour la conception d'objets technolo-giques tres complexes. Il est tentant de poursuivre ces efforts et de les orienter vers d'autres sujets particulierement complexes comme l'etude du cerveau. Il est cependant tres important de bien analyser les principes de la demarche de modelisation et de simulation pour les appliquer au mieux dans un cadre systemique, le plus adapte pour etudier le cerveau, et de se rendre compte ainsi qu'il ne s'agit pas de construire les modeles les plus precis et les plus lourds mais les plus adaptes a la question que l'on se pose.
Brain Informatics, 2021
The brain is a complex system, due to the heterogeneity of its structure, the diversity of the fu... more The brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. A systemic description of the brain is presented here, as a contribution to developing a brain theory and as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the...
Classical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generall... more Classical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.
In the context of flexible and adaptive animal behavior, the orbitofrontal cortex (OFC) is found ... more In the context of flexible and adaptive animal behavior, the orbitofrontal cortex (OFC) is found to be one of the crucial regions in the prefrontal cortex (PFC) influencing the downstream processes of decision-making and learning in the sub-cortical regions. Although OFC has been implicated to be important in a variety of related behavioral processes, the exact mechanisms are unclear, through which the OFC encodes or processes information related to decision-making and learning. Here, we propose a systems-level view of the OFC, positioning it at the nexus of sub-cortical systems and other prefrontal regions. Particularly we focus on one of the most recent implications of neuroscientific evidences regarding the OFC - possible functional dissociation between two of its sub-regions : lateral and medial. We present a system-level computational model of decision-making and learning involving the two sub-regions taking into account their individual roles as commonly implicated in neurosci...
Lecture Notes in Computer Science, 2016
One of the most critical properties of a versatile intelligent agent is its capacity to adapt aut... more One of the most critical properties of a versatile intelligent agent is its capacity to adapt autonomously to any change in the environment without overly complexifying its cognitive architecture. In this paper, we propose that understanding the role of neuromodulation in the brain is of central interest for this purpose. More precisely, we propose that an accurate estimation of the nature of uncertainty present in the environment is performed by specific brain regions and broadcast throughout the cerebral network by neuromodulators, resulting in appropriate changes in cerebral functioning and learning modes. Better understanding the principles of these mechanisms in the brain might tremendously inspire the field of Artificial General Intelligence. The original contribution of this paper is to relate the four major neuromodulators to four fundamental dimensions of uncertainty.
Biosystems & Biorobotics, 2015
Studying and modeling the brain as a whole is a real challenge. For such systemic models (in cont... more Studying and modeling the brain as a whole is a real challenge. For such systemic models (in contrast to models of one brain area or aspect), there is a real need for new tools designed to perform complex numerical experiments, beyond usual tools distributed in the computer science and neuroscience communities. Here, we describe an effective solution, freely available on line and already in use, to validate such models of the brain functions. We explain why this is the best choice, as a complement to robotic setup, and what are the general requirements for such a benchmarking platform. In this experimental setup, the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital interoceptive cues, complex survival behaviors can be experimented. We also discuss here algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier. The key point is to possibly alternate the use of symbolic representation and of complementary and usual neural coding. As a consequence, algorithmic principles have to be considered at higher abstract level, beyond a given data representation, which is an interesting challenge.
We present a novel way to design a control system for a robot, using emotions as a way to produce... more We present a novel way to design a control system for a robot, using emotions as a way to produce richer internal states. We believe that using a single scalar as an evaluation of the quality of the policy and stating that the goal of the agent is to gather reward, as it is proposed by reinforcement learning, is not an appropriate granularity for creating an autonomous control system : even with a fine-tuned reward function, efficient on a specific task, it is often impractical to derive any useful knoweldge from it in order to build more flexible, neuromimetics control systems. A complete shift of paradigm is necessary for a bottom up approach : the robot is given pain and pleasure perception circuits and we examine how emotions arise and are the basis for respondant and operant conditioning. Inspired from the cerebral circuits of superior mammals responsible of this behavior, we propose an implementation in an autonomous robot using models of adaptive neural networks.
HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific re... more HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Biological Cybernetics, 2015
The superior colliculus (SC) is a brainstem structure at the crossroad of multiple functional pat... more The superior colliculus (SC) is a brainstem structure at the crossroad of multiple functional pathways. Several neurophysiological studies suggest that the population of active neurons in the SC encodes the location of a visual target to foveate, pursue or attend to. Although extensive research has been carried out on computational modeling, most of the reported models are often based on complex mechanisms and explain a limited number of experimental results. This suggests that a key aspect may have been overlooked in the design of previous computational models. After a careful study of the literature, we hypothesized that the representation of the whole retinal stimulus (not only its center) might play an important role in the dynamics of SC activity. To test
The hallmark of most artificial neural networks is their supposed intrinsic parallelism where eac... more The hallmark of most artificial neural networks is their supposed intrinsic parallelism where each unit is evaluated concurrently to other units in a distributed way. However, if one gives a closer look under the hood, one can soon realize that such a parallelism is an illusion since most implementations use what is referred to as synchronous evaluation. The present article propose to consider different evaluation methods (namely asynchronous evaluation methods) and to study their properties in some restricted but illustrative cases.
Proceedings of 13th International Conference on Pattern Recognition, 1996
As mentioned above, these neuronal mechanisms are not new. Nevertheless, their putting together i... more As mentioned above, these neuronal mechanisms are not new. Nevertheless, their putting together in a simple unit and their association to cortical architecture is an original view, compatible with many biological data. In our view, CCP is not theoretical biology and biological plausibility is not a goal but a mean. In any case, cortical structure and functioning is the result of a long phylogenetical process and numerous ideas can fruitfully be adopted and adapted to engineering. From its origin, CCP reaches higher and higher cognitive capabilities. It leads to a better understanding of cerebral functioning, but also and above all to better implementation of real world applications. Indeed, its main interest is to be perceptually grounded and to build high-level cognitive functions on perceptive processing. Ongoing and future works include deepening of these models and particularly of their abilities for temporal processing in order to tackle more cognitive tasks. This should help building bridges between such domains as pattern recognition and artificial intelligence, but also active vision and autonomous learning.
Proceedings of the International Conference on Neural Computation Theory and Applications, 2014
Artificial Neural Networks are very efficient adaptive models but one of their recognized weaknes... more Artificial Neural Networks are very efficient adaptive models but one of their recognized weaknesses is about information representation, often carried out in an input vector without a structure. Beyond the classical elaboration of a hierarchical representation in a series of layers, we report here inspiration from neuroscience and argue for the design of heterogenous neural networks, processing information at feature, configuration and history levels of granularity, and interacting very efficiently for high-level and complex decision making. This framework is built from known characteristics of the sensory cortex, the hippocampus and the prefrontal cortex and is exemplified here in the case of pavlovian conditioning, but we propose that it can be advantageously applied in a wider extent, to design flexible and versatile information processing with neuronal computation.
Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics, 2014
Considering the experimental study of systemic models of the brain as a whole (in contrast to mod... more Considering the experimental study of systemic models of the brain as a whole (in contrast to models of one brain area or aspect), there is a real need for tools designed to realistically simulate these models and to experiment them. We explain here why a robotic setup is not necessarily the best choice, and what are the general requirements for such a bench-marking platform. A step further, we describe an effective solution, freely available on line and already in use to validate functional models of the brain. This solution is a digital platform where the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital somesthetic cues, complex survival behaviors can be experimented. The platform is also complemented with algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier.
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Papers by Frederic Alexandre