Making Decisions with Unknown Sensory Reliability

To make fast and accurate behavioral choices, we need to integrate noisy sensory input, take prior knowledge into account, and adjust our decision criteria. It was shown previously that in two-alternative-forced-choice tasks, optimal decision making can be formalized in the framework of a sequential probability ratio test and is then equivalent to a diffusion model. However, this analogy hides a “chicken and egg” problem: to know how quickly we should integrate the sensory input and set the optimal decision threshold, the reliability of the sensory observations must be known in advance. Most of the time, we cannot know this reliability without first observing the decision outcome. We consider here a Bayesian decision model that simultaneously infers the probability of two different choices and at the same time estimates the reliability of the sensory information on which this choice is based. We show that this can be achieved within a single trial, based on the noisy responses of sensory spiking neurons. The resulting model is a non-linear diffusion to bound where the weight of the sensory inputs and the decision threshold are both dynamically changing over time. In difficult decision trials, early sensory inputs have a stronger impact on the decision, and the threshold collapses such that choices are made faster but with low accuracy. The reverse is true in easy trials: the sensory weight and the threshold increase over time, leading to slower decisions but at much higher accuracy. In contrast to standard diffusion models, adaptive sensory weights construct an accurate representation for the probability of each choice. This information can then be combined appropriately with other unreliable cues, such as priors. We show that this model can account for recent findings in a motion discrimination task, and can be implemented in a neural architecture using fast Hebbian learning

A normative approach to radicalization in social networks

In recent decades, the massification of online social connections has made information globally accessible in a matter of seconds. Unfortunately, this has been accompanied by a dramatic surge in extreme opinions, without a clear solution in sight. Using a model performing probabilistic inference in large-scale loopy graphs through exchange of messages between nodes, we show how circularity in the social graph directly leads to radicalization and the polarization of opinions. We demonstrate that these detrimental effects could be avoided by actively decorrelating the messages in social media feeds. This approach is based on an extension of Belief Propagation (BP) named Circular Belief Propagation (CBP) that can be trained to drastically improve inference within a cyclic graph. CBP was benchmarked using data from Facebook and Twitter. This approach could inspire new methods for preventing the viral spreading and amplification of misinformation online, improving the capacity of social networks to share knowledge globally without resorting to censorship.Comment: 23 pages, 8 figures, 1 supplementary materia

Examination of the interaction between NCoA coactivator proteins in the regulation of transcription

Die Mitglieder der NCoA-Koaktivator-Familie fungieren als Koaktivatoren für verschiedene Transkriptionsfaktoren, wie z.B. nukleäre Hormonrezeptoren und STAT-Proteine. NCoA-Proteine rekrutieren sekundäre Koaktivatoren, die durch die Modifikation des Chromatins die Transkriptionsaktivierung ermöglichen. Vorhergehende Studien postulierten die Dimerisierung von NCoA-Proteinen über die aminoterminalen bHLH/PAS-Domänen und die Rekrutierung von Paaren von NCoA-Proteinen, konnten jedoch eine direkte Interaktion nicht nachweisen. In unserer Arbeitsgruppe konnte gezeigt werden, dass die PAS-B-Domäne von NCoA-1 ein LXXLL-Motiv in der Transaktivierungsdomäne von STAT6 binden kann. Im Rahmen dieser Arbeit sollte untersucht werden, ob eine Interaktion von Mitgliedern der NCoA-Proteinfamilie über die PAS-B-Domäne und eigene LXXLL-Motive vermittelt werden kann und welche physiologische Bedeutung die Interaktion von NCoA-Proteinen hat. Die Interaktion endogener NCoA-Proteine konnte in zwei verschiedenen Zelllinien nachgewiesen werden. Es konnte gezeigt werden, dass die PAS-B-Domänen aller drei NCoA-Familienmitglieder mit allen Volllängen-NCoA-Proteinen interagieren können und für eine solche Interaktion ausreichend sind. Dabei interagieren die PAS-B-Domänen spezifisch mit einer Region in der CBP-Interaktions-Domäne (CID/AD1) von NCoA-1, die zwei LXXLL-Motive und den vollständigen Bereich, der die Interaktion mit CBP vermittelt, enthält. Es zeigte sich, dass sich die Bindungsmotivspezifität der NCoA-1-PAS-B-Domäne von den Bindungsmotivspezifitäten der PAS-B-Domänen von NCoA-2 und NCoA-3 unterscheidet. Ebenso zeigten sich unterschiedliche Bindungsmotivspezifitäten für die Interaktion mit der CID/AD1 von NCoA-3, die nur mit der PAS-B-Domäne von NCoA-1 interagierte. Eine physiologische Bedeutung der charakterisierten PAS-B/CID/AD1-Interaktion auf die Bildung und Rekrutierung von Koaktivator-Komplexen wurde mittels Überexpressions-Experimenten untersucht, in denen dominant negative Effekte erwartet wurden. So führte die Überexpression der PAS-B-Domäne bzw. die Kompetition mit der CID/AD1 zur Inhibition der Interaktion von NCoA-1 mit dem Koaktivator CBP und dem Transkriptionsfaktor STAT6. Außerdem führte die stabile Überexpression der PAS-B-Domänen von NCoA-1 und NCoA-3 zu einer veränderten Expression des natürlichen endogenen Androgen-Rezeptor-Zielgenes PSA. Die in dieser Arbeit identifizierte Interaktion von NCoA-Proteinen stellt einen neuen und, zu den bisher bekannten Modellen der Koaktivator-Rekrutierung, ergänzenden Mechanismus dar. Dies gilt sowohl für eine postulierte inter- und intramolekulare Interaktion von NCoA-1 bei der STAT6-vermittelten Transkriptionsaktivierung, als auch für die durch nukleäre Hormonrezeptoren geforderte Rekrutierung von Paaren von NCoA-Proteinen. Zusammenfassend können die in dieser Arbeit erhaltenen Ergebnisse dabei helfen, das Verständnis der dynamischen Rekrutierung von Koaktivatoren bzw. Koaktivator-Komplexen und damit der Regulation der Genexpression, weiter zu verbessern.The members of the NCoA coactivator family function as coactivators for different transcription factors, like nuclear hormone receptors or STAT proteins. NCoA proteins recruit secondary coactivators, which in turn modify the chromatin, thus enabling the activation of transcription. Previous studies postulated dimerization of NCoA proteins through the aminoterminal bHLH/PAS domains and the recruitment of NCoA protein pairs, but a direct interaction has not yet been proven. Our group showed that the PAS-B domain of NCoA-1 binds to an LXXLL motif in the transactivation domain of STAT6. The aim of this work was, to examine whether an interaction of members of the NCoA protein family is mediated through the PAS-B domain and their own LXXLL motifs and to determine the physiological importance of an interaction between NCoA proteins. The interaction of endogenous NCoA proteins was detected in two different cell lines. It was shown that the PAS-B domains of all three NCoA family members are able to interact with all full-length NCoA proteins and that these domains are sufficient for these interactions. The PAS-B domains interact specifically with a region in the CBP interaction domain (CID/AD1) of NCoA-1, which contains two LXXLL motifs and the complete region, which mediates the interaction with CBP. Further analysis revealed that the binding motif specificity of the NCoA-1 PAS-B domain differs from the binding motif specificities of the PAS-B domains of NCoA-2 and NCoA-3. Likewise, different binding motif specificities were detected for the interaction with the CID/AD1 of NCoA-3, which interacted only with the PAS-B domain of NCoA-1. The physiological importance of the characterized PAS-B/CID/AD1 interaction for the formation and the recruitment of coactivator complexes was examined with overexpression experiments. Overexpression of the PAS-B domain and competition with the CID/AD1 led to inhibition of the interaction between NCoA-1 with the coactivator CBP or the transcription factor STAT6, respectively. Furthermore, stable overexpression of the PAS-B domain of NCoA-1 and NCoA-3 led to an altered expression of the natural endogenous androgen receptor target gene PSA. The identified interaction of NCoA proteins suggests a new and complementary mechanism for the known models of coactivator recruitment. This can be considered for the postulated inter- and intramolecular interaction of NCoA-1 in the STAT6 mediated transcriptional activation, as well as for the nuclear hormone receptor mediated recruitment of NCoA protein pairs. In summary, the results of this work can help to improve the understanding of the dynamic recruitment of coactivators and coactivator complexes and in turn the regulation of gene expression

Bayesian inference in spiking neurons

We propose a new interpretation of spiking neurons as Bayesian integrators accumulating evidence over time about events in the external world or the body, and communicating to other neurons their certainties about these events. In this model, spikes signal the occurrence of new information, i.e. what cannot be predicted from the past activity. As a result, firing statistics are close to Poisson, albeit providing a deterministic representation of probabilities. We proceed to develop a theory of Bayesian inference in spiking neural networks, recurrent interactions implementing a variant of belief propagation. Many perceptual and motor tasks performed by the central nervous system are probabilistic, and can be described in a Bayesian framework [4, 3]. A few important but hidden properties, such as direction of motion, or appropriate motor commands, are inferred from many noisy, local and ambiguous sensory cues. These evidences are combined with priors about the sensory world and body. Importantly, because most of these inferences shoul

Contextual modulation of auditory responses predicted by statistical inference

Auditory neurons exhibit complex nonlinear and context-dependent responses that are difficult to capture by standard modeling techniques. Rather than using a bottom up approach and characterizing the responses of individual cells as a function of their input, we propose to use a top-down, normative approach to analyzing auditory processing. We develop a model of spiking neurons that perform probabilistic inference to estimate the state of auditory environment from sensory signals. The model predicts a form for the nonlinear, context-dependent modulation of inputs to central auditory neurons. We show that a simple model based on auditory inference can explain the presence of multiple aspects of contextual modulation in both frequency and time that are observed in the auditory system

Inférence probabiliste et mémoire de travail dans des réseaux récurrents de neurones intègre-et-tire

Des études comportementales ont démontré que les sujets humains sont capables de prendre des décisions optimales malgré l'incertitude inhérente aux tâches perceptives ou motrices. Une question clé en neurosciences est de comprendre comment des populations de neurones à impulsions peuvent mettre en œuvre de tels calculs probabilistes. Dans cette thèse, nous développons un cadre global pour l'intégration sensorielle optimale et la mémoire de travail probabiliste dans des réseaux récurrents de neurones intègre-et-tire. Ces réseaux peuvent combiner des signaux sensoriels de manière optimale, suivre l'état d'un stimulus dynamique dans le temps, et mémoriser cette information accumulée sur des périodes beaucoup plus longues que la constante de temps des neurones individuels. Surtout, nous proposons que les réponses neuronales pendant l intégration d information et la mémoire de travail représentent non seulement la valeur du stimulus la plus probable, mais une distribution de probabilité sur l'ensemble des valeurs possibles du stimulus. Dans notre modèle, les neurones sont des codeurs prédictifs qui génèrent des potentiels d actions uniquement lorsqu'ils représentent de nouvelles informations pas encore signalées. Ceci constitue une différence importante avec les codes par taux de décharge, dans lesquels les potentiels d'action individuels sont considérés comme des échantillons aléatoires du taux de décharge sous-jacent. Dans le système de codage proposé, une multitude de motifs d activités peut encoder les mêmes informations de manière fiable. Cette variabilité ne peut pas être assimilée à du bruit. Au contraire, elle est une conséquence directe de l'inférence optimalePARIS-BIUSJ-Biologie recherche (751052107) / SudocSudocFranceF