Innovative Individuals Are Not Always the Best Demonstrators: Feeding Innovation and Social Transmission in Serinus canaria

Background: Feeding innovation occurs when individuals choose a novel, unknown type of food and/or acquire new feeding skills. Here we studied feeding innovation and social transmission of the new feeding habit in canaries. Adult canaries eat a wide variety of seeds but avoid larger ones such as those of sunflowers. We determined whether adults of both sexes are equally prone to innovate when confronted with sunflower seeds and whether free-interactions facilitate transmission of the new feeding habit in a sex-dependent manner. Methodology/Principal Findings: First we determined which sex was more innovative, i.e., was more successful at husking and eating the novel seeds. Males were clearly more innovative than females. Due to this, experienced males served as model for either male or female observers in three different conditions (free interaction with a demonstrator, visual interaction with a demonstrator placed behind a transparent wall and access to seeds in the presence of a nondemonstrating bird). During free interactions, the new feeding habit was only transmitted to females. In contrast, transmission of seed handling to male observers only occurred if demonstrator and observer were separated by the transparent wall. Indeed, aggressive behaviors between males prevented social transmission during free interactions. Finally, we studied the influence of the less innovative females in feeding-habit transmission. First, we obtained female demonstrators by making them freely interact with male demonstrators. Once they acquired innovative responses t

The Neural Representation Benchmark and its Evaluation on Brain and Machine

A key requirement for the development of effective learning representations is their evaluation and comparison to representations we know to be effective. In natural sensory domains, the community has viewed the brain as a source of inspiration and as an implicit benchmark for success. However, it has not been possible to directly test representational learning algorithms directly against the representations contained in neural systems. Here, we propose a new benchmark for visual representations on which we have directly tested the neural representation in multiple visual cortical areas in macaque (utilizing data from [Majaj et al., 2012]), and on which any computer vision algorithm that produces a feature space can be tested. The benchmark measures the effectiveness of the neural or machine representation by computing the classification loss on the ordered eigendecomposition of a kernel matrix [Montavon et al., 2011]. In our analysis we find that the neural representation in visual area IT is superior to visual area V4. In our analysis of representational learning algorithms, we find that three-layer models approach the representational performance of V4 and the algorithm in [Le et al., 2012] surpasses the performance of V4. Impressively, we find that a recent supervised algorithm [Krizhevsky et al., 2012] achieves performance comparable to that of IT for an intermediate level of image variation difficulty, and surpasses IT at a higher difficulty level. We believe this result represents a major milestone: it is the first learning algorithm we have found that exceeds our current estimate of IT representation performance. We hope that this benchmark will assist the community in matching the representational performance of visual cortex and will serve as an initial rallying point for further correspondence between representations derived in brains and machines.Comment: The v1 version contained incorrectly computed kernel analysis curves and KA-AUC values for V4, IT, and the HT-L3 models. They have been corrected in this versio

Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals

Ventral visual stream neural responses are dynamic, even for static image presentations. However, dynamical neural models of visual cortex are lacking as most progress has been made modeling static, time-averaged responses. Here, we studied population neural dynamics during face detection across three cortical processing stages. Remarkably, ~30 milliseconds after the initially evoked response, we found that neurons in intermediate level areas decreased their responses to typical configurations of their preferred face parts relative to their response for atypical configurations even while neurons in higher areas achieved and maintained a preference for typical configurations. These hierarchical neural dynamics were inconsistent with standard feedforward circuits. Rather, recurrent models computing prediction errors between stages captured the observed temporal signatures. This model of neural dynamics, which simply augments the standard feedforward model of online vision, suggests that neural responses to static images may encode top-down prediction errors in addition to bottom-up feature estimates.National Institutes of Health (U.S.) (Grant R01-EY014970)National Institutes of Health (U.S.) (Grant K99-EY022671)National Institutes of Health (U.S.) (Grant F32-EY019609)National Institutes of Health (U.S.) (Grant F32-EY022845)United States. Office of Naval Research (MURI-114407)McGovern Institute for Brain Research at MI

L’épopée ferroviaire Migrations et mémoire de la colonisation dans le récit contemporain

Les diverses représentations littéraires du train depuis son invention reflètent l’Histoire : la révolution industrielle, la résistance des cheminots, la déportation, mais aussi une dimension encore trop souvent négligée, la colonisation et les guerres de décolonisation. Exploitation de l’Afrique via la construction de réseaux ferrés depuis la seconde moitié du XIXe siècle et grève des cheminots en Afrique de l’Ouest (Sembène, Senghor) ; possibilité, lors de l’exposition coloniale de 1931, de faire le tour des pavillons de l’empire français à bord d’un train (Daeninckx) ; massacre d’octobre 1961 et affaire de la station de métro Charonne pendant la guerre d’Algérie (Sebbar). Ces événements historiques hantent depuis lors le système ferroviaire de la France métropolitaine. L’épopée du rail n’est donc plus un hymne au progrès mais le nouveau cadre des récits de migration qui réactive la mémoire de la colonisation (Boudjedra, Djemaï, Kerangal, Mabanckou, Maspero)

Object Recognition: Physiological and Computational Insights

Visual object recognition is the identification of a thing in the outside world based on the sense of vision. Our eyes are bombarded by a wide variety of visual forms, from simple shapes like cups an

La théorie des quasi-espèces : concepts, application à la dynamique des populations de virus a ARN, implications biologiques et limites

La théorie des quasi-espèces a été développée à partir d’un modèle mathématique pour caractériser le comportement des populations virales à ARN. Cette synthèse bibliographique vise dans un premier temps à rappeler la structure et les cycles viraux, ainsi qu’à définir les différents critères de la génétique des populations qui pourraient être utilisés dans l’étude des populations virales. Dans un second temps, les bases du modèle sont définies et les méthodes d’analyses de séquences au sein des populations sont présentées. Ces concepts permettent d’aborder la complexité et la dynamique qui caractérisent les quasi-espèces. Ils sont recensés dans les résultats expérimentaux et théoriques de la littérature. Ils ont de possibles implications quant au comportement biologique et évolutif des populations virales, et apportent un nouveau point de vue sur la thérapeutique antivirale. Enfin, la pertinence de ces propriétés est nuancée en exposant les limites mais aussi les extensions possibles à d’autres systèmes biologiques

Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream

Humans recognize visually-presented objects rapidly and accurately. To understand this ability, we seek to construct models of the ventral stream, the series of cortical areas thought to subserve object recognition. One tool to assess the quality of a model of the ventral stream is the Representational Dissimilarity Matrix (RDM), which uses a set of visual stimuli and measures the distances produced in either the brain (i.e. fMRI voxel responses, neural firing rates) or in models (fea-ures). Previous work has shown that all known models of the ventral stream fail to capture the RDM pattern observed in either IT cortex, the highest ventral area, or in the human ventral stream. In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream. The model, while novel in the optimization procedure, further develops a long-standing functional hypothesis that the ventral visual stream is a hierarchically arranged series of processing stages optimized for visual object recognition