2.1 Summary

At the frontier between integrative and computational neuroscience, we propose to model the brain as a system of active memories in synergy and in interaction with the internal and external world and to simulate it as a whole and in situation.

In integrative and cognitive neuroscience (cf. § 3.1), on the basis of current knowledge and experimental data, we develop models of the main cerebral structures, taking a specific care of the kind of mnemonic function they implement and of their interface with other cerebral and external structures. Then, in a systemic approach, we build the main behavioral loops involving these cerebral structures, connecting a wide spectrum of actions to various kinds of sensations. We observe at the behavioral level the properties emerging from the interaction between these loops.

We claim that this approach is particularly fruitful for investigating cerebral structures like the basal ganglia and the prefrontal cortex, difficult to comprehend today because of the rich and multimodal information flows they integrate. We expect to cope with the high complexity of such systems, inspired by behavioral and developmental sciences, explaining how behavioral loops gradually incorporate in the system various kinds of information and associated mnesic representations. As a consequence, the underlying cognitive architecture, emerging from the interplay between these sensations-actions loops, results from a mnemonic synergy.

In computational neuroscience (cf. § 3.2), we concentrate on the efficiency of local mechanisms and on the effectiveness of the distributed computations at the level of the system. We also take care of the analysis of their dynamic properties, at different time scales. These fundamental properties are of high importance to allow the deployment of very large systems and their simulation in a framework of high performance computing

Running simulations at a large scale is particularly interesting to evaluate over a long period a consistent and relatively complete network of cerebral structures in realistic interaction with the external and internal world. We face this problem in the domain of autonomous robotics (cf. § 3.4) and ensure a real autonomy by the design of an artificial physiology and convenient learning protocoles.

We are convinced that this original approach also permits to revisit and enrich algorithms and methodologies in machine learning (cf. § 3.3) and in autonomous robotics (cf. § 3.4), in addition to elaborate hypotheses to be tested in neuroscience and medicine, while offering to these latter domains a new ground of experimentation similar to their daily experimental studies.

3.1 Integrative and Cognitive Neuroscience

The human brain is often considered as the most complex system dedicated to information processing. This multi-scale complexity, described from the metabolic to the network level, is particularly studied in integrative neuroscience, the goal of which is to explain how cognitive functions (ranging from sensorimotor coordination to executive functions) emerge from (are the result of the interaction of) distributed and adaptive computations of processing units, displayed along neural structures and information flows. Indeed, beyond the astounding complexity reported in physiological studies, integrative neuroscience aims at extracting, in simplifying models, regularities at various levels of description. From a mesoscopic point of view, most neuronal structures (and particularly some of primary importance like the cortex, cerebellum, striatum, hippocampus) can be described through a regular organization of information flows and homogenous learning rules, whatever the nature of the processed information. From a macroscopic point of view, the arrangement in space of neuronal structures within the cerebral architecture also obeys a functional logic, the sketch of which is captured in models describing the main information flows in the brain, the corresponding loops built in interaction with the external and internal (bodily and hormonal) world and the developmental steps leading to the acquisition of elementary sensorimotor skills up to the most complex executive functions.

In summary, integrative neuroscience builds, on an overwhelming quantity of data, a simplifying and interpretative grid suggesting homogenous local computations and a structured and logical plan for the development of cognitive functions. They arise from interactions and information exchange between neuronal structures and the external and internal world and also within the network of structures.

This domain is today very active and stimulating because it proposes, of course at the price of simplifications, global views of cerebral functioning and more local hypotheses on the role of subsets of neuronal structures in cognition. In the global approaches, the integration of data from experimental psychology and clinical studies leads to an overview of the brain as a set of interacting memories, each devoted to a specific kind of information processing 64. It results also in longstanding and very ambitious studies for the design of cognitive architectures aiming at embracing the whole cognition. With the notable exception of works initiated by 58, most of these frameworks (e.g. Soar, ACT-R), though sometimes justified on biological grounds, do not go up to a connectionist neuronal implementation. Furthermore, because of the complexity of the resulting frameworks, they are restricted to simple symbolic interfaces with the internal and external world and to (relatively) small-sized internal structures. Our main research objective is undoubtly to build such a general purpose cognitive architecture (to model the brain as a whole in a systemic way), using a connectionist implementation and able to cope with a realistic environment.

3.2 Computational Neuroscience

From a general point of view, computational neuroscience can be defined as the development of methods from computer science and applied mathematics, to explore more technically and theoretically the relations between structures and functions in the brain 66, 57. During the recent years this domain has gained an increasing interest in neuroscience and has become an essential tool for scientific developments in most fields in neuroscience, from the molecule to the system. In this view, all the objectives of our team can be described as possible progresses in computational neuroscience. Accordingly, it can be underlined that the systemic view that we promote can offer original contributions in the sense that, whereas most classical models in computational neuroscience focus on the better understanding of the structure/function relationship for isolated specific structures, we aim at exploring synergies between structures. Consequently, we target interfaces and interplay between heterogenous modes of computing, which is rarely addressed in classical computational neuroscience.

We also insist on another aspect of computational neuroscience which is, in our opinion, at the core of the involvement of computer scientists and mathematicians in the domain and on which we think we could particularly contribute. Indeed, we think that our primary abilities in numerical sciences imply that our developments are characterized above all by the effectiveness of the corresponding computations: we provide biologically inspired architectures with effective computational properties, such as robustness to noise, self-organization, on-line learning. We more generally underline the requirement that our models must also mimick biology through its most general law of homeostasis and self-adaptability in an unknown and changing environment. This means that we propose to numerically experiment such models and thus provide effective methods to falsify them.

Here, computational neuroscience means mimicking original computations made by the neuronal substratum and mastering their corresponding properties: computations are distributed and adaptive; they are performed without an homonculus or any central clock. Numerical schemes developed for distributed dynamical systems and algorithms elaborated for distributed computations are of central interest here 52, 53 and were the basis for several contributions in our group 65, 61, 67. Ensuring such a rigor in the computations associated to our systemic and large scale approach is of central importance.

Equally important is the choice for the formalism of computation, extensively discussed in the connectionist domain. Spiking neurons are today widely recognized of central interest to study synchronization mechanisms and neuronal coupling at the microscopic level 59; the associated formalism 68 can be possibly considered for local studies or for relating our results with this important domain in connectionism. Nevertheless, we remain mainly at the mesoscopic level of modeling, the level of the neuronal population, and consequently interested in the formalism developed for dynamic neural fields 62, that demonstrated a richness of behavior 63 adapted to the kind of phenomena we wish to manipulate at this level of description. Our group has a long experience in the study and adaptation of the properties of neural fields 61, 60 and their use for observing the emergence of typical cortical properties 55. In the envisioned development of more complex architectures and interplay between structures, the exploration of mathematical properties such as stability and boundedness and the observation of emerging phenomena is one important objective. This objective is also associated with that of capitalizing our experience and promoting good practices in our software production.

In summary, we think that this systemic approach also brings to computational neuroscience new case studies where heterogenous and adaptive models with various time scales and parameters have to be considered jointly to obtain a mastered substratum of computation. This is particularly critical for large scale deployments.

3.3 Machine Learning

The adaptive properties of the nervous system are certainly among its most fascinating characteristics, with a high impact on our cognitive functions. Accordingly, machine learning is a domain 56 that aims at giving such characteristics to artificial systems, using a mathematical framework (probabilities, statistics, data analysis, etc.). Some of its most famous algorithms are directly inspired from neuroscience, at different levels. Connectionist learning algorithms implement, in various neuronal architectures, weight update rules, generally derived from the hebbian rule, performing non supervised (e.g. Kohonen self-organizing maps), supervised (e.g. layered perceptrons) or associative (e.g. Hopfield recurrent network) learning. Other algorithms, not necessarily connectionist, perform other kinds of learning, like reinforcement learning. Machine learning is a very mature domain today and all these algorithms have been extensively studied, at both the theoretical and practical levels, with much success. They have also been related to many functions (in the living and artificial domains) like discrimination, categorisation, sensorimotor coordination, planning, etc. and several neuronal structures have been proposed as the substratum for these kinds of learning 54, 50. Nevertheless, we believe that, as for previous models, machine learning algorithms remain isolated tools, whereas our systemic approach can bring original views on these problems.

At the cognitive level, most of the problems we face do not rely on only one kind of learning and require instead skills that have to be learned in preliminary steps. That is the reason why cognitive architectures are often referred to as systems of memory, communicating and sharing information for problem solving. Instead of the classical view in machine learning of a flat architecture, a more complex network of modules must be considered here, as it is the case in the domain of deep learning. In addition, our systemic approach brings the question of incrementally building such a system, with a clear inspiration from developmental sciences. In this perspective, modules can generate internal signals corresponding to internal goals, predictions, error signals, able to supervise the learning of other modules (possibly endowed with a different learning rule), supposed to become autonomous after an instructing period. A typical example is that of episodic learning (in the hippocampus), storing declarative memory about a collection of past episods and supervising the training of a procedural memory in the cortex.

At the behavioral level, as mentionned above, our systemic approach underlines the fundamental links between the adaptive system and the internal and external world. The internal world includes proprioception and interoception, giving information about the body and its needs for integrity and other fundamental programs. The external world includes physical laws that have to be learned and possibly intelligent agents for more complex interactions. Both involve sensors and actuators that are the interfaces with these worlds and close the loops. Within this rich picture, machine learning generally selects one situation that defines useful sensors and actuators and a corpus with properly segmented data and time, and builds a specific architecture and its corresponding criteria to be satisfied. In our approach however, the first question to be raised is to discover what is the goal, where attention must be focused on and which previous skills must be exploited, with the help of a dynamic architecture and possibly other partners. In this domain, the behavioral and the developmental sciences, observing how and along which stages an agent learns, are of great help to bring some structure to this high dimensional problem.

At the implementation level, this analysis opens many fundamental challenges, hardly considered in machine learning : stability must be preserved despite on-line continuous learning; criteria to be satisfied often refer to behavioral and global measurements but they must be translated to control the local circuit level; in an incremental or developmental approach, how will the development of new functions preserve the integrity and stability of others? In addition, this continous re-arrangement is supposed to involve several kinds of learning, at different time scales (from msec to years in humans) and to interfer with other phenomena like variability and meta-plasticity.

In summary, our main objective in machine learning is to propose on-line learning systems, where several modes of learning have to collaborate and where the protocoles of training are realistic. We promote here a really autonomous learning, where the agent must select by itself internal resources (and build them if not available) to evolve at the best in an unknown world, without the help of any deus-ex-machina to define parameters, build corpus and define training sessions, as it is generally the case in machine learning. To that end, autonomous robotics (cf. § 3.4) is a perfect testbed.

3.4 Autonomous Robotics

Autonomous robots are not only convenient platforms to implement our algorithms; the choice of such platforms is also motivated by theories in cognitive science and neuroscience indicating that cognition emerges from interactions of the body in direct loops with the world (embodiment of cognition51). In addition to real robotic platforms, software implementations of autonomous robotic systems including components dedicated to their body and their environment will be also possibly exploited, considering that they are also a tool for studying conditions for a real autonomous learning.

A real autonomy can be obtained only if the robot is able to define its goal by itself, without the specification of any high level and abstract cost function or rewarding state. To ensure such a capability, we propose to endow the robot with an artificial physiology, corresponding to perceive some kind of pain and pleasure. It may consequently discriminate internal and external goals (or situations to be avoided). This will mimick circuits related to fundamental needs (e.g. hunger and thirst) and to the preservation of bodily integrity. An important objective is to show that more abstract planning capabilities can arise from these basic goals.

A real autonomy with an on-line continuous learning as described in  § 3.3 will be made possible by the elaboration of protocols of learning, as it is the case, in animal conditioning, for experimental studies where performance on a task can be obtained only after a shaping in increasingly complex tasks. Similarly, developmental sciences can teach us about the ordered elaboration of skills and their association in more complex schemes. An important challenge here is to translate these hints at the level of the cerebral architecture.

As a whole, autonomous robotics permits to assess the consistency of our models in realistic condition of use and offers to our colleagues in behavioral sciences an object of study and comparison, regarding behavioral dynamics emerging from interactions with the environment, also observable at the neuronal level.

In summary, our main contribution in autonomous robotics is to make autonomy possible, by various means corresponding to endow robots with an artificial physiology, to give instructions in a natural and incremental way and to prioritize the synergy between reactive and robust schemes over complex planning structures.

4.1 Overview

Modeling the brain to emulate cognitive functions offers direct and indirect application domains. Our models are designed to be confronted to the reality of life sciences and to make predictions in neuroscience and in the medical domain. Our models also have an impact in digital sciences; their performances can be questioned in informatics, their algorithms can be compared with models in machine learning and artificial intelligence, their behavior can be analysed in human-robot interaction. But since what they produce is related to human thinking and behavior, applications will be also possible in various domains of social sciences and humanities.

4.2 Applications in life sciences

One of the most original specificity of our team is that it is part of a laboratory in Neuroscience (with a large spectrum of activity from the molecule to the behavior), focused on neurodegenerative diseases and consequently working in tight collaboration with the medical domain. Beyond data and signal analysis where our expertise in machine learning may be possibly useful, our interactions are mainly centered on the exploitation of our models. They will be classically regarded as a way to validate biological assumptions and to generate new hypotheses to be investigated in the living. Our macroscopic models and their implementation in autonomous robots will allow an analysis at the behavioral level and will propose a systemic framework, the interpretation of which will meet aetiological analysis in the medical domain and interpretation of intelligent behavior in cognitive neuroscience and related domains like for example educational science.

The study of neurodegenerative diseases is targeted because they match the phenomena we model. Particularly, the Parkinson disease results from the death of dopaminergic cells in the basal ganglia, one of the main systems that we are modeling. The Alzheimer disease also results from the loss of neurons, in several cortical and extracortical regions. The variety of these regions, together with large mnesic and cognitive deficits, require a systemic view of the cerebral architecture and associated functions, very consistent with our approach.

4.3 Application in digital sciences

Of course, digital sciences are also impacted by our researches, at several levels. At a global level, we will propose new control architectures aimed at providing a higher degree of autonomy to robots, as well as machine learning algorithms working in more realistic environment. More specifically, our focus on some cognitive functions in closed loop with a real environment will address currently open problems. This is obviously the case for planning and decision making; this is particularly the case for the domain of affective computing, since motivational characteristics arising from the design of an artificial physiology allow to consider not only cold rational cognition but also hot emotional cognition. The association of both kinds of cognition is undoubtly an innovative way to create more realistic intelligent systems but also to elaborate more natural interfaces between these systems and human users.

At last, we think that our activities in well-founded distributed computations and high performance computing are not just intended to help us design large scale systems. We also think that we are working here at the core of informatics and, accordingly, that we could transfer some fundamental results in this domain.

4.4 Applications in human sciences

Because we model specific aspects of cognition such as learning, language and decision, our models could be directly analysed from the perspective of educational sciences, linguistics and economy. Because we manipulate such concepts as embodiment of cognition, theory of mind and emotions, we could also participate in debates in philosophy and ethics.

Futhermore, our implication in science outreach actions, including computer science teaching in secondary and primary school, with the will to analyse and evaluate the outcomes of these actions, is at the origin of building a link between our research in computational learning and human learning, providing not only tools but also new modeling paradigms.

5.1 Footprint of research activities

As part of the Institute of Neurodegenerative Diseases that developed a strong commitment to the environment, we take our share in the reduction of our carbon footprint by deciding to reduce our commuting footprint and the number of yearly travels to conference. In 2021, this collided again with the sanitary crisis that de facto reduced our travel capacity. But beyond the crisis, we aim at enforcing this policy.

5.2 Impact of research results

We're engaged in the EcoMob regional project in collaboration with the University of Bordeaux and the University of La Rochelle to study and model the behavior of individuals during their daily trips to and from work places. In this context and based on our previous work on decision making, our team is interested in elucidating how habits are formed and more importantly, how can they be changed. This year, we participated to an activity of dissemination on this topic (and were awarded for that, cf. § 11.3.3).

6.1 Awards

X. Hinaut was awarded a young investigator ANR grant for the project DeepPool: "Hierarchical reservoirs to model language processing and production". This will enable to extend our studies on hierarchical reservoir computing 3 and sensorimotor models 25.

F. Alexandre and N. Rougier were awarded a Talents U 2021 for their active participation to the Climackathon initiative of university of Bordeaux that took place on March 12 and 13, 2021. During two half days, 30 students, scientists and staff co-created solutions to the following problem: how to engage the university community on climate change and motivate behavioral change ?

6.2 Recrutiment and promotions

A. Aussel has been recruited on an ISFP position.

N. Rougier has been promoted to a research director position.

6.3 Events

N. Rougier co-organized the first edition of a recurrent Science & Fiction festival (Hypermondes) mixing literature and sciences. The topic of the first edition was about Robotics. It gathered more than 40 SF authors, 15 scientists and 2500 people during the three days of the festival.

N. Rougier published an open access book on scientific visualization 5 which totaled 100,000 downloads in one week.

7.1 New software

8.1 Overview

This year we have addressed several important questions related to our scientific positioning. Central to this positioning, we have studied and modeled bio-inspired learning mechanisms and collaborative mnesic functions (cf. § 8.2). We have also studied higher cognitive functions, related to cognitive control (cf. § 8.3) and have also considered how important characteristics can be associated to this framework, like symbolic abstract knowledge (cf. § 8.4), and oscillations (cf. § 8.5). Endly, we have also pursued our work on language processing in birds and robots (cf. § 8.6).

8.2 Learning mechanisms and collaborative mnesic functions

This year, we have published a paper summarizing our general approach and proposing a global framework integrating the various mnesic functions and their interactions 1. We have been working on several bio-plausible learning mechanisms like hebbian learning, contrastive divergent learning (CDL) and reinforcement learning and their combination in different frameworks. This is the case for the development of original versions of Restricted Boltzmann Machines in robotic applications (cf. § 9.1.1) and for the development of Reservoir Computing. Within the ReservoirPy library (developed in the team) we have included several offline and online learning mechanisms, parallel implementation and various advanced features. We worked on the parallelisation with the HiePACS project-team. This new release of ReservoirPy (v0.3.0) enables to build complex reservoir architectures (we give some examples as python notebooks). We demonstrated the training speed of the parallel implementation in a study on birdsong decoding 26: we included a comparison with LSTMs which should to be much slower and perform worse on few data than reservoirs. Additionally, we proposed a general method to search reservoir hyperparameters for any task, using visual tools proposed in ReservoirPy 21.

We've also investigated the development of automatized skills, such as song production in the songbird that is driven by the basal ganglia through dopamine-modulated reinforcement learning in order to guide learning in a parallel "cortical" pathway, which eventually governs the production of the skill 6. From a theoretical perspective, this can be considered as a dual learning system, one being reactive and dependent on instantaneous reward (reinforcement learning) while the other being much slower and independent of reward but being able to strongly bias the output. This hypothesis is now tested in the newt where the absence of a proper cortex should prevent such dual learning.

Due to the parallel development of a reservoir canary decoder 26 and a canary syllable GAN generator 34, it enables to build a full vocal sensorimotor model for songbird imitation learning 25. It enables a model to create real canary syllables of high quality (most were indistinguishable from real canary sounds). This enables the sensorimotor model to learn from its own real sounds produced.

8.3 Cognitive control

In cognitive control, the working memory in the prefrontal cortex and the episodic memory in the hippocampus play a major role in inhibiting the dominant behavior and replacing it with contextual rules in a flexible way, as we have studied in 20. This year, we have pursued or work on the definition of a flexible switching between rules, thus stressing the difference between simple and abstract rules, and we have begun to develop a model of the hippocampus to better understand the role of episodic memory in cognitive control 18, including its role for creativity 19.

8.4 Integrating abstract symbolic knowledge

Within the AIDE AEx (cf. § 10.3.3), we introduced the idea of a symbolic description of a complex human learning task, in order to contribute to better understanding how we learn 23, in the very precise framework of a task, named #CreaCube, related to initiation to computational thinking presented as an open-ended problem, which involves solving a problem and appealing to creativity. We also proposed to map an ontology onto a SPA-based architecture with a preliminary partial implementation into spiking neural networks 23 in order to provide an effective link between symbolic presentation of information and biologically plausible numerical implementation, including regarding representation of belief, revisiting the possibility theory 36. We furthermore proposed to make explicit how a reinforcement learning paradigm can be applied to a symbolic representation of a concrete problem-solving task, modeled here by an ontology 22. This work is embedded in a strong collaboration with education science collaborators 30 working on computational thinking initiation and computer science tools in education with a multi-disciplinary vision of cognitive function modeling 33.

Still at the interface between numerical and symbolic approaches, we have also proposed an original work to extract rules learned by a Long Short Term Memory model fed with sequences built from an unknown grammar 9.

8.5 Integrating oscillations

Theta-nested gamma oscillations have been reported in many areas of the brain and are believed to constitute a fundamental mechanism to transfer information across spatial and temporal scales. Such transfer is known to occur between hippocampus and prefrontal cortex and this is the reason that leads us to first investigate oscillations inside a detailed model of the hippocampus based on the model by our new colleague A. Aussel. We introduced in this model a set of Kuramoto oscillators in order to model the oscillatory properties of the medial septum. These oscillators are able to induce a theta reset in the presence of a strong sensory input as it has been observed in rodents.

8.6 Language processing

We made a hierarchical task reservoir able to parse natural language starting from speech inputs, towards several levels of abstraction (phonemes, words, part-of-speech), until the semantic role labels 3. This demonstrate the ability of reservoirs to perform abstract complex tasks in the language domain requiring embedded levels of abstraction.

A reservoir-based syllable canary decoder was implemented using ReservoirPy 26, it enables to build a full vocal sensorimotor model for songbird imitation learning 25. We have implemented a GAN able to generate high quality songbird sounds (canary syllables) 34. An additional novelty is that the generator part of the GAN is low-dimensional; with as few as 2 or 3 dimensions it is able to faithfully reproduce and interpolate between canary syllables. This low-dimensional property enabled to include it in a full sensorimotor model 25: a simple Hebbian learning mechanism is sufficient to learn the motor commands to learn to imitate canary syllables.

We build on previous cross-situational learning experiments towards robot language grounding, and made an extensive comparison on different datasets related to robot instructions, various word embedding representations, and comparing various architectures (reservoirs, LSTMs, ...) 38.

9.1 Bilateral contracts with industry

10.1 International initiatives

10.2 European initiatives

10.3 National initiatives

10.4 Regional initiatives

11.1 Promoting scientific activities

11.2 Teaching - Supervision - Juries

11.3 Popularization

12.1 Major publications

• 1 articleF.Frédéric Alexandre. A global framework for a systemic view of brain modeling.Brain Informatics81February 2021, 22
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• 2 articleA.Aurélien Nioche, N. P.Nicolas P. Rougier, M.Marc Deffains, S.Sacha Bourgeois-Gironde, S.Sébastien Ballesta and T.Thomas Boraud. The adaptive value of probability distortion and risk-seeking in macaques' decision-making.Philosophical Transactions of the Royal Society B: Biological SciencesJanuary 2021
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• 3 articleL.Luca Pedrelli and X.Xavier Hinaut. Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech.IEEE Transactions on Neural Networks and Learning SystemsSeptember 2021
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• 4 articleN. .Nicolas P. Rougier and G. I.Georgios Is Detorakis. Randomized Self Organizing Map.Neural Computation2021
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• 5 bookN. .Nicolas P. Rougier. Scientific Visualization: Python + Matplotlib.November 2021
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• 6 articleR.Remya Sankar, N. .Nicolas P. Rougier and A.Arthur Leblois. Computational benefits of structural plasticity, illustrated in songbirds.Neuroscience & Biobehavioral Reviews2021
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12.2 Publications of the year

12.3 Other

12.4 Cited publications

• 50 inproceedingsF.Frédéric Alexandre. Biological Inspiration for Multiple Memories Implementation and Cooperation.International Conference on Computational Intelligence2000
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• 51 articleD. H.Dana H. Ballard, M. M.Mary M. Hayhoe, P. K.Polly K. Pook and R. P.Rajesh P. N. Rao. Deictic codes for the embodiment of cognition.Behavioral and Brain Sciences20041997, 723--742URL: http://dx.doi.org/10.1017/S0140525X97001611
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• 52 bookD.P.~Bertsekas and J.N.~Tsitsiklis. Parallel and Distributed Computation: Numerical Methods.Athena Scientific1997
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• 53 articleD.~Mitra. Asynchronous relaxations for the numerical solution of differential equations by parallel processors.SIAM J. Sci. Stat. Comput.811987, 43--58
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• 54 articleK.Kenji Doya. What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?Neural Networks121999, 961--974
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• 55 articleJ.Jérémy Fix, N. P.Nicolas P. Rougier and F.Frédéric Alexandre. A dynamic neural field approach to the covert and overt deployment of spatial attention.Cognitive Computation312011, 279-293URL: http://hal.inria.fr/inria-00536374/en
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• 56 bookT.Tom Mitchell. Machine Learning.Mac Graw-Hill Press1997
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• 57 bookP.~Dayan and L.F.~Abbott. Theoretical Neuroscience : Computational and Mathematical Modeling of Neural Systems.MIT Press2001
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• 58 bookR.C.~O'Reilly and Y.~Munakata. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain.Cambridge, MA, USAMIT Press2000
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• 59 articleR.~Brette, M.~Rudolph, T.~Carnevale, M.~Hines, D.~Beeman, J.J.M.~ Bower, M.~Diesmann, A.A. Morrison, P. H.P. H. Goodman, F. C.F. C. Jr. Harris, M.M. Zirpe, T.~Natschläger, D.~Pecevski, B.~Ermentrout, M.~Djurfeldt, A.~Lansner, O.~Rochel, T.~Viéville, E.~Muller, A.P.~Davison, S.S.~El Boustani and A.~Destexhe. Simulation of networks of spiking neurons: a review of tools and strategies.Journal of Computational Neuroscience2332007, 349--398
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• 60 articleN. P.Nicolas P. Rougier. Dynamic Neural Field with Local Inhibition.Biological Cybernetics9432006, 169-179
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• 61 articleN. P.Nicolas P. Rougier and A.Axel Hutt. Synchronous and Asynchronous Evaluation of Dynamic Neural Fields.J. Diff. Eq. Appl.2009
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• 62 articleS.~Amari. Dynamic of pattern formation in lateral-inhibition type neural fields.Biological Cybernetics271977, 77--88
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• 63 articleS.~Coombes. Waves, bumps and patterns in neural field theories.Biol. Cybern.932005, 91-108
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• 64 articleL.L.R. Squire. Memory systems of the brain: a brief history and current perspective.Neurobiol. Learn. Mem.822004, 171-177
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• 65 articleW.Wahiba Taouali, T.Thierry Viéville, N. P.Nicolas P. Rougier and F.Frédéric Alexandre. No clock to rule them all.Journal of Physiology1051-32011, 83-90
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• 66 bookT.T.P. Trappenberg. Fundamentals of Computational Neuroscience.Oxford University Press2002
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• 67 articleT.Thierry Viéville. An unbiased implementation of regularization mechanisms.Image and Vision Computing23112005, 981--998URL: http://authors.elsevier.com/sd/article/S0262885605000909
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• 68 bookW.~Gerstner and W.M.~Kistler. Spiking Neuron Models: Single Neurons, Populations, Plasticity.Cambridge University PressCambridge University Press2002
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