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Population coding—the transmission of information by the combined activity of many neurons—is a feature of many neural systems. Identifying the role played by individual neurons within a population code is vital for the understanding of neural codes. In this thesis I examine which stimuli are best encoded by a given neuron within a population and how this depends on the informational measure used, on commonly-measured neuronal properties, and on the population size and the spacing between stimuli. I also show how correlative measures of topography can be used to test for significant topography in the anatomical arrangement of arbitrary neuronal properties. The neurons involved in a population code are generally clustered together in one region of the brain, and moreover their response selectivity is often reflected in their anatomical arrangement within that region. Although such topographic maps are an often-encountered feature in the brains of many species, there are no standard, objective procedures for quantifying topography. Topography in neural maps is typically identified and described subjectively, but in cases where the scale of the map is close to the resolution limit of the measurement technique, identifying the presence of a topographic map can be a challenging subjective task. In such cases, an objective statistical test for detecting topography would be advantageous. To address these issues, I assess seven measures by quantifying topography in simulated neural maps, and show that all but one of these are effective at detecting statistically significant topography even in weakly topographic maps. The precision of the neural code is commonly investigated using two different families of statistical measures: (i) Shannon mutual information and derived quantities when investigating very small populations of neurons and (ii) Fisher information when studying large populations. The Fisher information always predicts that neurons convey most information about stimuli coinciding with the steepest regions of the tuning curve, but it is known that information theoretic measures can give very different predictions. Using a Monte Carlo approach to compute a stimulus-specific decomposition of the mutual information (the stimulus-specific information, or SSI) for populations up to hundreds of neurons in size, I address the following questions: (i) Under what conditions can Fisher information accurately predict the information transmitted by a neuron within a population code? (ii) What are the effects of level of trial-to-trial variability (noise), correlations in the noise, and population size on the best-encoded stimulus? (iii) How does the type of task in a behavioural experiment (i.e. fine and coarse discrimination, classification) affect the best-encoded stimulus? I show that, for both unimodal and monotonic tuning curves, the shape of the SSI is dependent upon trial-to-trial variability, population size and stimulus spacing, in addition to the shape of the tuning curve. It is therefore important to take these factors into account when assessing which stimuli a neuron is informative about; just knowing the tuning curve may not be sufficient.

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<p>The visual system is thought to represent the trajectory of moving objects in the activity of large populations of cortical neurons that respond preferentially to the direction of stimulus motion. Here I employed in vivo voltage sensitive dye (VSD) imaging to explore how abrupt changes in the trajectory of a moving stimulus impact the population coding of motion direction in ferret primary visual cortex (V1). For motion in a constant direction, the peak of the cortical population response reliably signaled the stimulus trajectory; but for abrupt changes in motion direction, the peak of the population response departed significantly from the stimulus trajectory in a fashion that depended on the size of the direction deviation. For small direction deviation angles, the peak of the active population shifted from values consistent with the initial direction of motion to those consistent with the final direction of motion by progressing smoothly through intermediate directions not present in the stimulus. In contrast, for large direction deviation angles, peak values consistent with the initial motion direction were followed by: a small deviation away from the final motion direction, a rapid 180° jump, and a gradual shift to the final direction. These departures of the population response from the actual trajectory of the stimulus predict specific misperceptions of motion direction that were confirmed by human psychophysical experiments. I conclude that cortical dynamics and population coding mechanisms combine to place constraints on the accuracy with which abrupt changes in direction of motion can be represented by cortical circuits.</p> / Dissertation

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Nos entrées sensorielles, comme nos mouvements, sont entachés d’incertitudes. Pourtant, notre système nerveux central semble être aussi précis que possible compte tenu de ces incertitudes: il les gère de manière optimale, par exemple en pondérant des informations sensorielles redondantes en fonction de leur fiabilité, ou en prenant en compte ses incertitudes motrices lors de la réalisation de mouvements. Si les modalités des combinaisons d’informations redondantes sont bien connues lors de tâches statiques, elles le sont moins en conditions dynamiques, lors de mouvements. La partie expérimentale de cette thèse a permis de confirmer l’existence de mécanismes d’estimation et de contrôle optimaux des mouvements chez l’humain. En effet, nous avons mis en évidence l’intégration optimale d’information visuelle lors de la réalisation de saccades à la lumière: lors de séquences de saccades, le système visuomoteur est capable d’utiliser l’information visuelle pour mettre à jour ses estimations internes de la position de l’œil. Une étude complémentaire des sources de variabilité des saccades suggère un rôle similaire pour la proprioception extra-oculaire. Par une troisième expérience, novatrice, nous avons montré que le toucher est pris en compte en temps réel lors de mouvements de la main en contact avec une surface. Nous avons également inféré une mesure de la variance de l'information tactile. Enfin, à partir des connaissances sur la représentation des variables sensorimotrices dans le système nerveux, nous avons construit plusieurs réseaux de neurones qui implémentent de manière proche de l'optimum statistique la planification et le contrôle de mouvements / Our sensory inputs, as well as our movements, are uncertain. Nevertheless, our central nervous systems appears to be as accurate as possible: these uncertainties are handled in an optimal fashion. For example, redundant sensory signals are weighted according to their accuracy, and motor uncertainties are taken in account when movements are made. The characteristics of the combination of redundant sensory signals are well known for static tasks. However, they are less known in dynamic conditions. The experimental part of this thesis allowed to confirm the use of statistically optimal sensorimotor processes during movements. We showed that visual information can be integrated during sequences of saccades, the oculomotor system being able to use visual information to update its internal estimate of eye position. A complementary study on the sources of variability for saccadic eye movements suggests a similar role for extra-ocular proprioception. In a third original experiment, we showed that tactile input is optimally taken in account for the on-line control of arm movements during which fingertips are in contact with a textured surface. Last, we built several neuronal networks models simulating optimal movement planning. These networks were based on current knowledge about probabilistic representations in the nervous system

This thesis deals with machine learning techniques for the extraction of structure and the analysis of the vertebrate olfactory pathway based on related methods. Some of its main contributions are summarized below. We have performed a systematic investigation for classification in biomedical images with the goal of recognizing a material in these images by its texture. This investigation included (i) different measures for evaluating the importance of image descriptors (features), (ii) methods to select a feature set based on these evaluations, and (iii) classification algorithms. Image features were evaluated according to their estimated relevance for the classification task and their redundancy with other features. For this purpose, we proposed a framework for relevance and redundancy measures and, within this framework, we proposed two new measures. These were the value difference metric and the fit criterion. Both measures performed well in comparison with other previously used ones for evaluating features. We also proposed a Hopfield network as a method for feature selection, which in experiments gave one of the best results relative to other previously used approaches. We proposed a genetic algorithm for clustering and tested it on several realworld datasets. This genetic algorithm was novel in several ways, including (i) the use of intra-cluster distance as additional optimization criterion, (ii) an annealing procedure, and (iii) adaptation of mutation rates. As opposed to many conventional clustering algorithms, our optimization framework allowed us to use different cluster validation measures including those which do not rely on cluster centroids. We demonstrated the use of the clustering algorithm experimentally with several cluster validity measures as optimization criteria. We compared the performance of our clustering algorithm to that of the often-used fuzzy c-means algorithm on several standard machine learning datasets from the University of California/Urvine (UCI) and obtained good results. The organization of representations in the brain has been observed at several stages of processing to spatially decompose input from the environment into features that are somehow relevant from a behavioral or perceptual standpoint. For the perception of smells, the analysis of such an organization, however, is not as straightforward because of the missing metric. Some studies report spatial clusters for several combinations of physico-chemical properties in the olfactory bulb at the level of the glomeruli. We performed a systematic study of representations based on a dataset of activity-related images comprising more than 350 odorants and covering the whole spatial array of the first synaptic level in the olfactory system. We found clustered representations for several physico-chemical properties. We compared the relevance of these properties to activations and estimated the size of the coding zones. The results confirmed and extended previous studies on olfactory coding for physico-chemical properties. Particularly of interest was the spatial progression by carbon chain that we found. We discussed our estimates of relevance and coding size in the context of processing strategies. We think that the results obtained in this study could guide the search into olfactory coding primitives and the understanding of the stimulus space. In a second study on representations in the olfactory bulb, we grouped odorants together by perceptual categories, such as floral and fruity. By the application of the same statistical methods as in the previous study, we found clustered zones for these categories. Furthermore, we found that distances between spatial representations were related to perceptual differences in humans as reported in the literature. This was possibly the first time that such an analysis had been done. Apart from pointing towards a spatial decomposition by perceptual dimensions, results indicate that distance relationships between representations could be perceptually meaningful. In a third study, we modeled axon convergence from olfactory receptor neurons to the olfactory bulb. Sensory neurons were stimulated by a set of biologically-relevant odors, which were described by a set of physico-chemical properties that covaried with the neural and glomerular population activity in the olfactory bulb. Convergence was mediated by the covariance between olfactory neurons. In our model, we could replicate the formation of glomeruli and concentration coding as reported in the literature, and further, we found that the spatial relationships between representational zones resulting from our model correlated with reported perceptual differences between odor categories. This shows that natural statistics, including similarity of physico-chemical structure of odorants, can give rise to an ordered arrangement of representations at the olfactory bulb level where the distances between representations are perceptually relevant. / <p>QC 20120224</p>

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