A stable sensory map emerges from a dynamic equilibrium of neurons with unstable tuning properties.

Recent long-term measurements of neuronal activity have revealed that, despite stability in large-scale topographic maps, the tuning properties of individual cortical neurons can undergo substantial reformatting over days. To shed light on this apparent contradiction, we captured the sound response dynamics of auditory cortical neurons using repeated 2-photon calcium imaging in awake mice. We measured sound-evoked responses to a set of pure tone and complex sound stimuli in more than 20,000 auditory cortex neurons over several days. We found that a substantial fraction of neurons dropped in and out of the population response. We modeled these dynamics as a simple discrete-time Markov chain, capturing the continuous changes in responsiveness observed during stable behavioral and environmental conditions. Although only a minority of neurons were driven by the sound stimuli at a given time point, the model predicts that most cells would at least transiently become responsive within 100 days. We observe that, despite single-neuron volatility, the population-level representation of sound frequency was stably maintained, demonstrating the dynamic equilibrium underlying the tonotopic map. Our results show that sensory maps are maintained by shifting subpopulations of neurons "sharing" the job of creating a sensory representation.

Predicting the Dynamics of Network Connectivity in the Neocortex.

Dynamic remodeling of connectivity is a fundamental feature of neocortical circuits. Unraveling the principles underlying these dynamics is essential for the understanding of how neuronal circuits give rise to computations. Moreover, as complete descriptions of the wiring diagram in cortical tissues are becoming available, deciphering the dynamic elements in these diagrams is crucial for relating them to cortical function. Here, we used chronic in vivo two-photon imaging to longitudinally follow a few thousand dendritic spines in the mouse auditory cortex to study the determinants of these spines' lifetimes. We applied nonlinear regression to quantify the independent contribution of spine age and several morphological parameters to the prediction of the future survival of a spine. We show that spine age, size, and geometry are parameters that can provide independent contributions to the prediction of the longevity of a synaptic connection. In addition, we use this framework to emulate a serial sectioning electron microscopy experiment and demonstrate how incorporation of morphological information of dendritic spines from a single time-point allows estimation of future connectivity states. The distinction between predictable and nonpredictable connectivity changes may be used in the future to identify the specific adaptations of neuronal circuits to environmental changes. The full dataset is publicly available for further analysis. Significance statement: The neural architecture in the neocortex exhibits constant remodeling. The functional consequences of these modifications are poorly understood, in particular because the determinants of these changes are largely unknown. Here, we aimed to identify those modifications that are predictable from current network state. To that goal, we repeatedly imaged thousands of dendritic spines in the auditory cortex of mice to assess the morphology and lifetimes of synaptic connections. We developed models based on morphological features of dendritic spines that allow predicting future turnover of synaptic connections. The dynamic models presented in this paper provide a quantitative framework for adding putative temporal dynamics to the static description of a neuronal circuit from single time-point connectomics experiments.

Co-expression of VAL- and TMT-opsins uncovers ancient photosensory interneurons and motorneurons in the vertebrate brain.

The functional principle of the vertebrate brain is often paralleled to a computer: information collected by dedicated devices is processed and integrated by interneuron circuits and leads to output. However, inter- and motorneurons present in today's vertebrate brains are thought to derive from neurons that combined sensory, integration, and motor function. Consistently, sensory inter-motorneurons have been found in the simple nerve nets of cnidarians, animals at the base of the evolutionary lineage. We show that light-sensory motorneurons and light-sensory interneurons are also present in the brains of vertebrates, challenging the paradigm that information processing and output circuitry in the central brain is shielded from direct environmental influences. We investigated two groups of nonvisual photopigments, VAL- and TMT-Opsins, in zebrafish and medaka fish; two teleost species from distinct habitats separated by over 300 million years of evolution. TMT-Opsin subclasses are specifically expressed not only in hypothalamic and thalamic deep brain photoreceptors, but also in interneurons and motorneurons with no known photoreceptive function, such as the typeXIV interneurons of the fish optic tectum. We further show that TMT-Opsins and Encephalopsin render neuronal cells light-sensitive. TMT-Opsins preferentially respond to blue light relative to rhodopsin, with subclass-specific response kinetics. We discovered that tmt-opsins co-express with val-opsins, known green light receptors, in distinct inter- and motorneurons. Finally, we show by electrophysiological recordings on isolated adult tectal slices that interneurons in the position of typeXIV neurons respond to light. Our work supports "sensory-inter-motorneurons" as ancient units for brain evolution. It also reveals that vertebrate inter- and motorneurons are endowed with an evolutionarily ancient, complex light-sensory ability that could be used to detect changes in ambient light spectra, possibly providing the endogenous equivalent to an optogenetic machinery.

A multiplicative reinforcement learning model capturing learning dynamics and interindividual variability in mice.

Both in humans and in animals, different individuals may learn the same task with strikingly different speeds; however, the sources of this variability remain elusive. In standard learning models, interindividual variability is often explained by variations of the learning rate, a parameter indicating how much synapses are updated on each learning event. Here, we theoretically show that the initial connectivity between the neurons involved in learning a task is also a strong determinant of how quickly the task is learned, provided that connections are updated in a multiplicative manner. To experimentally test this idea, we trained mice to perform an auditory Go/NoGo discrimination task followed by a reversal to compare learning speed when starting from naive or already trained synaptic connections. All mice learned the initial task, but often displayed sigmoid-like learning curves, with a variable delay period followed by a steep increase in performance, as often observed in operant conditioning. For all mice, learning was much faster in the subsequent reversal training. An accurate fit of all learning curves could be obtained with a reinforcement learning model endowed with a multiplicative learning rule, but not with an additive rule. Surprisingly, the multiplicative model could explain a large fraction of the interindividual variability by variations in the initial synaptic weights. Altogether, these results demonstrate the power of multiplicative learning rules to account for the full dynamics of biological learning and suggest an important role of initial wiring in the brain for predispositions to different tasks.

Dynamics of dendritic spines in the mouse auditory cortex during memory formation and memory recall.

Long-lasting changes in synaptic connections induced by relevant experiences are believed to represent the physical correlate of memories. Here, we combined chronic in vivo two-photon imaging of dendritic spines with auditory-cued classical conditioning to test if the formation of a fear memory is associated with structural changes of synapses in the mouse auditory cortex. We find that paired conditioning and unpaired conditioning induce a transient increase in spine formation or spine elimination, respectively. A fraction of spines formed during paired conditioning persists and leaves a long-lasting trace in the network. Memory recall triggered by the reexposure of mice to the sound cue did not lead to changes in spine dynamics. Our findings provide a synaptic mechanism for plasticity in sound responses of auditory cortex neurons induced by auditory-cued fear conditioning; they also show that retrieval of an auditory fear memory does not lead to a recapitulation of structural plasticity in the auditory cortex as observed during initial memory consolidation.

Deep and precise quantification of the mouse synaptosomal proteome reveals substantial remodeling during postnatal maturation.

During postnatal murine maturation, behavioral patterns emerge and become shaped by experience-dependent adaptations. During the same period, the morphology of dendritic spines, the morphological correlates of excitatory synapses, is known to change, and there is evidence of concurrent alterations of the synaptosomal protein machinery. To obtain comprehensive and quantitative insights in the developmental regulation of the proteome of synapses, we prepared cortical synaptosomal fractions from a total of 16 individual juvenile and adult mouse brains (age 3 or 8 weeks, respectively). We then applied peptide-based iTRAQ labeling (four pools of 4 animals) and high-resolution two-dimensional peptide fractionation (99 SCX fractions and 3 h reversed-phase gradients) using a hybrid CID-HCD acquisition method on a Velos Orbitrap mass spectrometer to identify a comprehensive set of synaptic proteins and to quantify changes in protein expression. We obtained a data set tracking expression levels of 3500 proteins mapping to 3427 NCBI GeneIDs during development with complete quantification data available for 3422 GeneIDs, which, to the best of our knowledge, constitutes the deepest coverage of the synaptosome proteome to date. The inclusion of biological replicates in a single mass spectrometry analysis demonstrated both high reproducibility of our synaptosome preparation method as well as high precision of our quantitative data (correlation coefficient R = 0.87 for the biological replicates). To evaluate the validity of our data, the developmental regulation of eight proteins identified in our analysis was confirmed independently using western blotting. A gene ontology analysis confirmed the synaptosomal nature of a large fraction of identified proteins. Of note, the set of the most strongly regulated proteins revealed candidates involved in neurological processes in health and disease states. This highlights the fact that developmentally regulated proteins can play additional roles in neurological disease processes. All data have been deposited to the ProteomeXchange with identifier PXD000552.

Validation of a German version of the Boredom Proneness Scale and the Multidimensional State Boredom Scale.

The scientific interest in boredom is growing over the past decades. Boredom has not only been linked to symptoms of psychopathology, but also shows a remarkable effect on individual behavior under healthy conditions. Current characterizations of boredom in humans mostly rely on self-report assessments which proved to faithfully reflect boredom in a vast range of experimental environments. Two of the most commonly used and prominent self-report scales in order to assess boredom are the Multidimensional State Boredom Scale (MSBS) and the Boredom Proneness Scale (BPS). Here, we present the German translations of both questionnaires and their validation. We obtained and analyzed psychometric data from more than 800 healthy individuals. We find that the German MSBS and BPS show vast congruence with their originals in respect to item statistics, internal reliability and validity. In particular, we find remarkable associations of state boredom and trait boredom with indicators of mental burden. Testing the factor structure of both questionnaires, we find supporting evidence for a 5-factor model of the MSBS, whereas the BPS in line with its original shows an irregular, inconsistent factor structure. Thus, we validate the German versions of MSBS and BPS and set a starting point for further studies of boredom in German-speaking collectives.

Representational maps in the brain: concepts, approaches, and applications.

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

Modeling fashion as an emergent collective behavior of bored individuals.

Boredom is an aversive mental state that is typically evoked by monotony and drives individuals to seek novel information. Despite this effect on individual behavior, the consequences of boredom for collective behavior remain elusive. Here, we introduce an agent-based model of collective fashion behavior in which simplified agents interact randomly and repeatedly choose alternatives from a circular space of color variants. Agents are endowed with a memory of past experiences and a boredom parameter, promoting avoidance of monotony. Simulating collective color trends with this model captures aspects of real trends observed in fashion magazines. We manipulate the two parameters and observe that the boredom parameter is essential for perpetuating fashion dynamics in our model. Furthermore, highly bored agents lead future population trends, when acting coherently or being highly popular. Taken together, our study illustrates that highly bored individuals can guide collective dynamics of a population to continuously explore different variants of behavior.

High state boredom vastly affects psychiatric inpatients and predicts their treatment duration.

Boredom is a ubiquitous, aversive human experience typically elicited by low information and monotony. Boredom can occur either as a transient mental state that prompts individuals to adapt their behavior to avoid monotony or as a temporally stable trait, describing a chronic susceptibility to feeling bored. Increased trait boredom was found to correlate with various psychopathologies and indicators of mental burden. However, the role of state boredom in psychopathological conditions and its implications for psychiatric treatment remain elusive. Here, we address this issue by investigating state boredom and trait boredom in a cohort of psychiatric inpatients and a healthy control cohort. We find that in both groups, state boredom, even more than trait boredom, shows remarkable associations with psychopathology. In the inpatient group, state boredom is implicated broadly in multiple mental disorders and shows an association with treatment in closed psychiatric wards. Furthermore, through statistical modeling, we find that high-state boredom during inpatient therapy is predictive of a longer therapy duration. Thus, we show that state boredom constitutes an indicator of mild and severe psychopathology in different mental disorders, affecting the outcome of psychiatric patients. Potential therapeutic interventions are discussed, aiming to enhance information flow in the brain in order to alleviate boredom in clinical settings.

Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging.

Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to simultaneously image large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for 3D spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data acquired by two-photon imaging in vivo. The core of our pipeline is a deep convolutional neural network that was pretrained on a general-purpose image library and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labeled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection reaching a performance slightly below that of the human experts. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.

An unbiased AAV-STARR-seq screen revealing the enhancer activity map of genomic regions in the mouse brain in vivo.

Enhancers are important cis-regulatory elements controlling cell-type specific expression patterns of genes. Furthermore, combinations of enhancers and minimal promoters are utilized to construct small, artificial promoters for gene delivery vectors. Large-scale functional screening methodology to construct genomic maps of enhancer activities has been successfully established in cultured cell lines, however, not yet applied to terminally differentiated cells and tissues in a living animal. Here, we transposed the Self-Transcribing Active Regulatory Region Sequencing (STARR-seq) technique to the mouse brain using adeno-associated-viruses (AAV) for the delivery of a highly complex screening library tiling entire genomic regions and covering in total 3 Mb of the mouse genome. We identified 483 sequences with enhancer activity, including sequences that were not predicted by DNA accessibility or histone marks. Characterizing the expression patterns of fluorescent reporters controlled by nine candidate sequences, we observed differential expression patterns also in sparse cell types. Together, our study provides an entry point for the unbiased study of enhancer activities in organisms during health and disease.

Multiplicative dynamics underlie the emergence of the log-normal distribution of spine sizes in the neocortex in vivo.

What fundamental properties of synaptic connectivity in the neocortex stem from the ongoing dynamics of synaptic changes? In this study, we seek to find the rules shaping the stationary distribution of synaptic efficacies in the cortex. To address this question, we combined chronic imaging of hundreds of spines in the auditory cortex of mice in vivo over weeks with modeling techniques to quantitatively study the dynamics of spines, the morphological correlates of excitatory synapses in the neocortex. We found that the stationary distribution of spine sizes of individual neurons can be exceptionally well described by a log-normal function. We furthermore show that spines exhibit substantial volatility in their sizes at timescales that range from days to months. Interestingly, the magnitude of changes in spine sizes is proportional to the size of the spine. Such multiplicative dynamics are in contrast with conventional models of synaptic plasticity, learning, and memory, which typically assume additive dynamics. Moreover, we show that the ongoing dynamics of spine sizes can be captured by a simple phenomenological model that operates at two timescales of days and months. This model converges to a log-normal distribution, bridging the gap between synaptic dynamics and the stationary distribution of synaptic efficacies.

Experienced entropy drives choice behavior in a boring decision-making task.

Boredom has been defined as an aversive mental state that is induced by the disability to engage in satisfying activity, most often experienced in monotonous environments. However, current understanding of the situational factors inducing boredom and driving subsequent behavior remains incomplete. Here, we introduce a two-alternative forced-choice task coupled with sensory stimulation of different degrees of monotony. We find that human subjects develop a bias in decision-making, avoiding the more monotonous alternative that is correlated with self-reported state boredom. This finding was replicated in independent laboratory and online experiments and proved to be specific for the induction of boredom rather than curiosity. Furthermore, using theoretical modeling we show that the entropy in the sequence of individually experienced stimuli, a measure of information gain, serves as a major determinant to predict choice behavior in the task. With this, we underline the relevance of boredom for driving behavioral responses that ensure a lasting stream of information to the brain.

Learning-induced biases in the ongoing dynamics of sensory representations predict stimulus generalization.

Sensory stimuli have long been thought to be represented in the brain as activity patterns of specific neuronal assemblies. However, we still know relatively little about the long-term dynamics of sensory representations. Using chronic in vivo calcium imaging in the mouse auditory cortex, we find that sensory representations undergo continuous recombination, even under behaviorally stable conditions. Auditory cued fear conditioning introduces a bias into these ongoing dynamics, resulting in a long-lasting increase in the number of stimuli activating the same subset of neurons. This plasticity is specific for stimuli sharing representational similarity to the conditioned sound prior to conditioning and predicts behaviorally observed stimulus generalization. Our findings demonstrate that learning-induced plasticity leading to a representational linkage between the conditioned stimulus and non-conditioned stimuli weaves into ongoing dynamics of the brain rather than acting on an otherwise static substrate.

Rapid nucleus-scale reorganization of chromatin in neurons enables transcriptional adaptation for memory consolidation.

The interphase nucleus is functionally organized in active and repressed territories defining the transcriptional status of the cell. However, it remains poorly understood how the nuclear architecture of neurons adapts in response to behaviorally relevant stimuli that trigger fast alterations in gene expression patterns. Imaging of fluorescently tagged nucleosomes revealed that pharmacological manipulation of neuronal activity in vitro and auditory cued fear conditioning in vivo induce nucleus-scale restructuring of chromatin within minutes. Furthermore, the acquisition of auditory fear memory is impaired after infusion of a drug into auditory cortex which blocks chromatin reorganization in vitro. We propose that active chromatin movements at the nucleus scale act together with local gene-specific modifications to enable transcriptional adaptations at fast time scales. Introducing a transgenic mouse line for photolabeling of histones, we extend the realm of systems available for imaging of chromatin dynamics to living animals.

Author Correction: Inhibitory connectivity defines the realm of excitatory plasticity.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cortical recruitment determines learning dynamics and strategy.

Salience is a broad and widely used concept in neuroscience whose neuronal correlates, however, remain elusive. In behavioral conditioning, salience is used to explain various effects, such as stimulus overshadowing, and refers to how fast and strongly a stimulus can be associated with a conditioned event. Here, we identify sounds of equal intensity and perceptual detectability, which due to their spectro-temporal content recruit different levels of population activity in mouse auditory cortex. When using these sounds as cues in a Go/NoGo discrimination task, the degree of cortical recruitment matches the salience parameter of a reinforcement learning model used to analyze learning speed. We test an essential prediction of this model by training mice to discriminate light-sculpted optogenetic activity patterns in auditory cortex, and verify that cortical recruitment causally determines association or overshadowing of the stimulus components. This demonstrates that cortical recruitment underlies major aspects of stimulus salience during reinforcement learning.

The Sensory Neocortex and Associative Memory.

Most behaviors in mammals are directly or indirectly guided by prior experience and therefore depend on the ability of our brains to form memories. The ability to form an association between an initially possibly neutral sensory stimulus and its behavioral relevance is essential for our ability to navigate in a changing environment. The formation of a memory is a complex process involving many areas of the brain. In this chapter we review classic and recent work that has shed light on the specific contribution of sensory cortical areas to the formation of associative memories. We discuss synaptic and circuit mechanisms that mediate plastic adaptations of functional properties in individual neurons as well as larger neuronal populations forming topographically organized representations. Furthermore, we describe commonly used behavioral paradigms that are used to study the mechanisms of memory formation. We focus on the auditory modality that is receiving increasing attention for the study of associative memory in rodent model systems. We argue that sensory cortical areas may play an important role for the memory-dependent categorical recognition of previously encountered sensory stimuli.

Inhibitory connectivity defines the realm of excitatory plasticity.

Recent experiments demonstrate substantial volatility of excitatory connectivity in the absence of any learning. This challenges the hypothesis that stable synaptic connections are necessary for long-term maintenance of acquired information. Here we measure ongoing synaptic volatility and use theoretical modeling to study its consequences on cortical dynamics. We show that in the balanced cortex, patterns of neural activity are primarily determined by inhibitory connectivity, despite the fact that most synapses and neurons are excitatory. Similarly, we show that the inhibitory network is more effective in storing memory patterns than the excitatory one. As a result, network activity is robust to ongoing volatility of excitatory synapses, as long as this volatility does not disrupt the balance between excitation and inhibition. We thus hypothesize that inhibitory connectivity, rather than excitatory, controls the maintenance and loss of information over long periods of time in the volatile cortex.