Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons

Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer {population models of interacting neurons that collectively encode stimulus information}. The key to disentangling intrinsic from extrinsic correlations is to infer the {couplings between neurons} separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus

Neural oscillations as a signature of efficient coding in the presence of synaptic delays

Cortical networks exhibit ‘global oscillations’, in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a ‘prediction error’ while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code

Human-in-the-Loop Optimization for Deep Stimulus Encoding in Visual Prostheses

Neuroprostheses show potential in restoring lost sensory function and enhancing human capabilities, but the sensations produced by current devices often seem unnatural or distorted. Exact placement of implants and differences in individual perception lead to significant variations in stimulus response, making personalized stimulus optimization a key challenge. Bayesian optimization could be used to optimize patient-specific stimulation parameters with limited noisy observations, but is not feasible for high-dimensional stimuli. Alternatively, deep learning models can optimize stimulus encoding strategies, but typically assume perfect knowledge of patient-specific variations. Here we propose a novel, practically feasible approach that overcomes both of these fundamental limitations. First, a deep encoder network is trained to produce optimal stimuli for any individual patient by inverting a forward model mapping electrical stimuli to visual percepts. Second, a preferential Bayesian optimization strategy utilizes this encoder to optimize patient-specific parameters for a new patient, using a minimal number of pairwise comparisons between candidate stimuli. We demonstrate the viability of this approach on a novel, state-of-the-art visual prosthesis model. We show that our approach quickly learns a personalized stimulus encoder, leads to dramatic improvements in the quality of restored vision, and is robust to noisy patient feedback and misspecifications in the underlying forward model. Overall, our results suggest that combining the strengths of deep learning and Bayesian optimization could significantly improve the perceptual experience of patients fitted with visual prostheses and may prove a viable solution for a range of neuroprosthetic technologies

Role of goal-orientated attention and expectations in visual processing and perception

Visual processing is not fixed, but changes dynamically depending on the spatiotemporal context of the presented stimulus, and the behavioural task being performed. In this thesis, I describe theoretical and experimental work that was conducted to investigate how and why visual perception and neural responses are altered by the behavioural and statistical context of presented stimuli. The process by which stimulus expectations are acquired and then shape our sensory experiences is not well understood. To investigate this, I conducted a psychophysics experiment where participants were asked to estimate the direction of motion of presented stimuli, with some directions presented more frequently than others. I found that participants quickly developed expectations for the most frequently presented directions and that this altered their perception of new stimuli, inducing biases in the perceived motion direction as well as visual hallucinations in the absence of a stimulus. These biases were well explained by a model that accounted for their behaviour using a Bayesian strategy, combining a learned prior of the stimulus statistics with their sensory evidence using Bayes’ rule. Altering the behavioural context of presented stimuli results in diverse changes to visual neuron responses, including alterations in receptive field structure and firing rates. While these changes are often thought to reflect optimization towards the behavioural task, what exactly is being optimized and why different tasks produce such varying effects is unknown. To account for the effects of a behavioural task on visual neuron responses, I extend previous Bayesian models of visual processing, hypothesizing that the brain learns an internal model that predicts how both the sensory input and the reward received for performing different actions are determined by a common set of explanatory causes. Short-term changes in visual neural responses would thus reflect optimization of this internal model to deal with changes in the sensory environment (stimulus statistics) and behavioural demands (reward statistics), respectively. This framework is used to predict a range of experimentally observed effects of goal-orientated attention on visual neuron responses. Together, these studies provide new insight into how and why sensory processing adapts in response to changes in the environment. The experimental results support the idea of a very plastic visual system, in which prior knowledge is rapidly acquired and used to shape perception. The theoretical work extends previous Bayesian models of sensory processing, to understand how visual neural responses are altered by the behavioural context of presetned stimuli. Finally, these studies provide a unified description of ‘expectations’ and ‘goal-orientated attention’, as corresponding to continuous adaptation of an internal generative model of the world to account for newly received contextual information

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