Attentional and Semantic Anticipations

Why are attentional processes important in the driving of anticipations? Anticipatory processes are fundamental cognitive abilities of living systems, in order to rapidly and accurately perceive new events in the environment, and to trigger adapted behaviors to the newly perceived events. To process anticipations adapted to sequences of various events in complex environments, the cognitive system must be able to run specific anticipations on the basis of selected relevant events. Then more attention must be given to events potentially relevant for the living system, compared to less important events. What are useful attentional factors in anticipatory processes? The relevance of events in the environment depend on the effects they can have on the survival of the living system. The cognitive system must then be able to detect relevant events to drive anticipations and to trigger adapted behaviors. The attention given to an event depends on i) its external physical relevance in the environment, such as time duration and visual quality, and ii) on its internal semantic relevance in memory, such as knowledge about the event (semantic field in memory) and anticipatory power (associative strength to anticipated associates). How can we model interactions between attentional and semantic anticipations? Specific types of distributed recurrent neural networks are able to code temporal sequences of events as associated attractors in memory. Particular learning protocol and spike rate transmission through synaptic associations allow the model presented to vary attentionally the amount of activation of anticipations (by activation or inhibition processes) as a function of the external and internal relevance of the perceived events. This type of model offers a unique opportunity to account for both anticipations and attention in unified terms of neural dynamics in a recurrent network

THE THABINA IRRIGATION SCHEME IN A CONTEXT OF REHABILITATION AND MANAGEMENT TRANSFER: PROSPECTIVE ANALYSIS AND LOCAL EMPOWERMENT

Resource /Energy Economics and Policy,

Dendara métropole

Le chantier « Dendara métropole » vise à étudier les divers aspects du temple d’Hathor dans son environnement, en portant les investigations sur l’étude architecturale des monuments ainsi que sur l’exploration archéologique des quartiers d’habitations et des cimetières. Outre la poursuite des travaux sur l’architecture monumentale, sur les secteurs associés aux fondations de Montouhotep II et sur la nécropole de l’Ancien Empire, la campagne 2019 a ouvert de nouvelles perspectives de recherche..

A proposed new bacteriophage subfamily: “Jerseyvirinae”

© 2015, Springer-Verlag Wien. Based on morphology and comparative nucleotide and protein sequence analysis, a new subfamily of the family Siphoviridae is proposed, named “Jerseyvirinae” and consisting of three genera, “Jerseylikevirus”, “Sp3unalikevirus” and “K1glikevirus”. To date, this subfamily consists of 18 phages for which the genomes have been sequenced. Salmonella phages Jersey, vB_SenS_AG11, vB_SenS-Ent1, vB_SenS-Ent2, vB_SenS-Ent3, FSL SP-101, SETP3, SETP7, SETP13, SE2, SS3e and wksl3 form the proposed genus “Jerseylikevirus”. The proposed genus “K1glikevirus” consists of Escherichia phages K1G, K1H, K1ind1, K1ind2 and K1ind3. The proposed genus “Sp3unalikevirus” contains one member so far. Jersey-like phages appear to be widely distributed, as the above phages were isolated in the UK, Canada, the USA and South Korea between 1970 and the present day. The distinguishing features of this subfamily include a distinct siphovirus morphotype, genomes of 40.7-43.6kb (49.6-51.4mol% G+C), a syntenic genome organisation, and a high degree of nucleotide sequence identity and shared proteins. All known members of the proposed subfamily are strictly lytic

Evaluation de l'impact des aides à l'investissement aux scieries en Auvergne et Limousin

This study evaluates the impact of investment subsidies received by sawmills on their demand for wood and labor. Because firms self select into the aid system, we use econometric panel data techniques to correct for selection bias. Difference in difference techniques and fixed effects are used to correct for permanent unobserved differences between firms receiving and not receiving subsidies. We find that the subsidies increased the demand for wood by 4 %, at a cost of 25 Euros by cubed meter, and increased the demand for labor by 1%.L'objectif de l'étude est d'évaluer l'impact du dispositif d'aide à l'investissement aux scieries existant en Auvergne et Limousin sur la période 1994/2003. On étudie l'impact des aides sur le niveau d'achats de grumes, l'emploi en scierie et de production de sciages. L'évaluation est quantitative : on applique les méthodes économétriques d'évaluation d'impact, notamment la technique de double différence. Les données de l'enquête annuelle de branche sont couplées aux données sur le système d'aide relevées par les services régionaux de la forêt et du bois (SRFB) des deux régions. L'étude a enfin pour objectif d'évaluer les performances du système d'aide mesurées par le montant de subvention nécessaire à provoquer l'achat d'un mètre cube de grumes supplémentaire ou d'un emploi supplémentaire. L'impact des aides sur la demande de grumes est important: en l'absence des aides, 70000m3 de bois n'auraient pas été achetés par les entreprises dans les deux régions chaque année, ce qui correspond à une hausse de 4%. Chaque m3 supplémentaire a coûté 25 Euros de subventions environ, si l'on considère que l'ensemble des montants publics mobilisés l'a été pour ce seul objectif. L'impact des aides sur l'emploi est faible. C'est la réception répétée d'aides (plus de trois fois pour la même entreprise) qui a des conséquences positives sur l'emploi. On peut considérer que 250 emplois ont été créés en 10 ans par le système d'aide, ce qui correspond à une hausse de 1%

[The SFBC scientific workgroups].

International audienceEditoria

Attentional and Semantic Anticipations in Recurrent Neural Networks

Why are attentional processes important in the driving of anticipations? Anticipatory processes are fundamental cognitive abilities of living systems, in order to rapidly and accurately perceive new events in the environment, and to trigger adapted behaviors to the newly perceived events. To process anticipations adapted to sequences of various events in complex environments, the cognitive system must be able to run specific anticipations on the basis of selected relevant events. Then more attention must be given to events potentially relevant for the living system, compared to less important events. What are useful attentional factors in anticipatory processes? The relevance of events in the environment depend on the effects they can have on the survival of the living system. The cognitive system must then be able to detect relevant events to drive anticipations and to trigger adapted behaviors. The attention given to an event depends on i) its external physical relevance in the environment, such as time duration and visual quality, and ii) on its internal semantic relevance in memory, such as knowledge abou

Neural Network Modeling of Learning of Contextual Constraints on Adaptive Anticipations

Anticipatory processes take into account of the contextual events occurring in the environment to anticipate probable upcoming events, and to select the best behavioral responses. The necessary knowledge for prediction of events adapted to context can be learned by classical associative conditioning, which allows associations between events occurring close in a sequence. Context can then correspond to events perceived in the environment as well as to the reinforcing valence of the event eliciting emotional states in the system, both orienting anticipations in memory. Knowledge for anticipation of adapted behaviors to context can be learned by operant reinforced conditioning, which allows associations between behaviors and reinforcing events in the environment, as a function of the reinforcing valence of the event (positive or negative). In this case the processing of a contextual event can select behavioral responses orienting the system to positive reinforcers rather than to negative reinforcers. An attractor neural network model is proposed to account for the different types of anticipatory processes presented as well as for the learning principles of conditioning allowing adapted anticipations