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International audience; We introduce the idea of a symbolic description of a complex human learning task, in order to contribute to better understand how we learn. The learner is modeled on the basis of knowledge from learning sciences with the contribution of cognitive neurosciences, including machine learning formalism, in the very precise framework of a task, named #CreaCube reviewed here, related to initiation to computational thinking presented as an open-ended problem, which involves solving a problem and appealing to creativity. We target problem-solving tasks using tangible interfaces for computational thinking initiation, and describe in details how we model the task and the learner behavior in this task, including goal-driven versus stimulus-driven behavior and the learner knowledge construction. We show how formalizing these elements using an ontology offers a well-defined computational model and the possibility of inferences about model elements, analyzing and predicting the learner behavior. This operationalization of a creative problem-solving task is still at a preliminary stage, but an effective proof of concept is described in this study.

International audience; This paper presents a new approach for learning transition function in state representation learning (SRL) for control. While state-of-the-art methods use different deterministic neural networks to learn forward and inverse state transition functions independently with auto-supervised learning, we introduce a bidirectional stochastic model to learn both transition functions. We aim at using the uncertainty of the model on its predictions as an intrinsic motivation for exploration to enhance the representation learning. More, using the same model to learn both transition functions allows sharing the parameters, which can reduce their number and should increase the embedding quality of the representation. We use a factored restricted Boltzmann machine (fRBM) based model, enhanced with dedicated structure for learning system dynamics and transitions with shared parameters. The presented work focuses on building the structure of the bidirectional transition model for unsupervised learning. Our fRBM structure is directly inspired from physics interactions between inputs and outputs in reinforcement learning framework. We compare different training algorithms for learning the model that must be able to predict observable random variables to be used in SRL framework. Our structure is not restricted to any type of observable, nevertheless in this paper we focus on learning dynamics from the OpenAI Gym environment Swinging Pendulum. We show that the proposed structure is able to learn bidirectional transition function and performs well in prediction task.

Basal Ganglia, a group of sub-cortical neuronal nuclei in the brain, are commonly described as the neuronal substratum to Reinforcement Learning. Since the seminal work by Schultz [1], a huge amount of work has been done to deepen that analogy, from functional and anatomic points of view. Nevertheless, a noteworthy architectural hint has been hardly explored: the outstanding reduction of dimensionality from the input to the output of the basal ganglia. Bar-Gad et al. [2] have suggested that this transformation could correspond to a Principal Component Analysis but did not explore the full functional consequences of this hypothesis. In this paper, we propose to study this mechanism within a model more realistic from a computational neuroscience point of view. Particularly, we show its feasibility when the loop is closed, in the framework of Action Selection.

The main aim in this paper is to study an algorithm of vigilance detection from a minimal number of EEG electrodes, easy to implement on programmable devices, to be used in ambulatory and real everyday life conditions. The connectionist unsupervised approach is summarized in this paper. From the unsupervised classification obtained, a connectionist supervised classification algorithm, the learning vector quantization (LVQ), is used for two different tasks. Firstly, the artefacted states are detected and removed. Secondly, the states deprived of artefacts are then classified in order to decide for the state of vigilance. Connectionist methods with supervised and unsupervised training were used to discriminate the EEG signals characterizing the vigilance states. An artificial neuronal model with a minimal architecture minimizes the complexity and allows implementation. It demonstrates that information, pertinent enough to characterize vigilance states, can be extracted from EEG signal recorded from a single electrode It should also be noted that the intervention of the expert is fundamental in this approach to differentiate nonartefacted vigilance states and artefacted vigilance states.

Colloque avec actes et comité de lecture.; Most models concerned with real-world applications can be improved in structuring data and incorporating knowledge about the domain. In our problem of radio electrical wave dying down prediction for mobile communication, a geographic database can be divided in contextual subsets, each representing an homogeneous domain where a predictive model performs better. More precisely, by clustering the input space, a predictive model (here a multilayer perceptron) can be trained on each subspace. Various unsupervised algorithms for clustering were evaluated (Kohonen's maps. Desieno's algorithm, Neural gas, Growing Neural Gas, Buhmann's algorithm) to obtain class homogeneous enough to decrease the predictive error of the radio electrical wave prediction

The authors propose a novel approach to neuronlike models that consists in simulating cortical columns, i.e. associations of neurons having a specific, functional activity. This model is built according to a theory which is consistent with neurobiological data. The model is implemented through a network of columns that simulate the inherently parallel functioning of the nervous system. A description is given of this model, and the experimental conditions in which it is now being tested are described. >

We propose a new architectural model to deal with applications where the input space is very complex and high-dimensional. This solution is inspired from a classical connectionist approach and avoids saturation (A. Guez et al., 1988) due to the use of another connectionist structure, for memory storage. This model is very useful for control problems where input spaces are often complex and heterogeneous. >

The neural net model TOM (temporal organization map) that we present in the paper is a new connectionist approach whose time representation is different from the one in classical temporal connectionist models. The architecture is neurobiologically inspired and is dedicated to sensory problems involving a temporal dimension. The basic idea of the TOM model is the propagation of an activity throughout the network whose elements are organized according to a map architecture. This propagation leads to a triggering of a sequence detection. We have applied this new kind of architecture to a spoken digit recognition problem. The results draw near to the results of the best hidden Markov model (HMM) techniques. The interest of such an architecture is its genericity and the possibility to merge several data flows in order to improve the classical performances of neural nets.

The purpose of this work is to deal with real world applications where the size of the problem and the influence of contextual parameters can lead to inefficiency of control including neuro-control. Indeed, the size of real world problems is often very high, especially if numerous contextual parameters are used. This often leads to a prohibitive size for neural networks taken in their monolithic version. On the other hand, the absence of this context will destinate the work to a specific domain of application. These remarks are all the more accurate as we know that this contextual parameters generally have a continuous effect on the process and that they could be considered as specific modulators of the application. We present here a learning algorithm for neuro-controllers with reduced number of inputs. In fact, the contextual parameters are only used to estimate the synaptic weights of the neuro-controller with other NNs. Moreover, the present version is an online economic solution to this problem. >

In this paper, a semi-empirical model of field strength prediction combining theoretical results of propagation loss algorithms and artificial neural networks is considered. This approach expects to overcome some limitations inherent in existing semi-empirical models: linear behaviour of the statistical analysis used in the construction of the models, unfitness for learning new situations. The good results obtained in a dense urban area show that neural networks are a very efficient empirical method to compute new kinds of models which integrate theoretical and experimental data.

From classical connectionist and symbolic models, the new field of neurosymbolic integration has emerged, whose aim is to benefit from the advantages of both domains to model human perceptive and cognitive capabilities. To reach this goal, some strategies are envisaged, among which connectionist cognitive processing claims that these desired capabilities can emerge from pure neuronal structures and processes. This approach refers to the substratum of cognition, the human brain and gives rise to perceptually grounded models whose goal is to reach higher cognitive levels. Its principles are presented here and, as an illustration, an application to invariant pattern recognition is described. From basic connectionist models, a biologically inspired model of neuronal networks cooperation is implemented to allow for internal information translation. This mechanism leads to automatic pattern centring in a classical character recognition application with excellent performances.

Presents the basic principles and some preliminary applications of a novel connectionist model for building up a neural network system. The model uses the cortical column instead of the classical neuron as the basic processing unit; it is closely related to the neurobiological modeling of the human cortex. The main features of this model which make it different from classical approaches concern the local connectivity and the integration of time by causality learning. The model provides a basic unit well -adapted to solve humanlike problems by integrating particular difficulties in its own structure (e.g. the coarticulation effect in speech recognition). The model has been applied to two difficult problems in the artificial intelligence field: speech recognition (more precisely, the acoustic-phonetic decoding of continuous speech) and biomedical X-ray image interpretation. The two systems that were designed for these applications demonstrate the ability of the cortical column to solve perceptive and cognitive tasks