Biology subjects

Friedrich, R. W.

Publications and source records attributed to Friedrich, R. W..

At least 19 recordsLinked to original sources

bioRxiv2026

Open-source virtual reality platform for head-fixed adult zebrafish

Virtual realities (VRs) are powerful tools to study how brains interact with the world through behavior. We present a new platform for closed-loop high-resolution virtual-reality experiments in head-fixed adult zebrafish. The VR is defined using open-source software, projected onto a semi-hexagonal tank by three projectors, and controlled by software allowing for modular trial-and-block experimental designs. Real time VR updates are performed by an optimized algorithm based on infrared tail videos. Two-photon imaging during behavior is achieved by custom gating of VR projectors and coordinated with stimulus triggers by a microcontroller. Using this system we found that fish respond to visual landmarks in virtual corridors with distinct changes in locomotion parameters and body posture. Adding landmarks to a familiar corridor elicited localized behavioral responses that decayed with experience. Restricting the viewing distance by simulating 'murky water' further confirmed that behavioral responses were driven by the visibility of landmarks independent of their position. Our platform provides a user-friendly, versatile VR system for the joint analysis of neuronal activity and behavior in adult zebrafish

neuroscience↗

bioRxiv2026

Fine ultrastructural organization of glomerular neuropil in the developing zebrafish olfactory bulb

Across animal phyla, odor information is represented in the first olfactory processing center by combinatorial activation of discrete glomeruli. To examine how this organization of olfactory processing channels emerges during development we combined 2-photon calcium imaging, volume electron microscopy, and large-scale automated neuron reconstruction in zebrafish larvae. We focused on a developmental stage (130 hours post fertilization) when the olfactory bulb (OB) is structured into ~16 large neuropil regions referred to as protoglomeruli but most glomeruli are not yet differentiated. Our ultrastructural reconstructions revealed that protoglomeruli are organized into smaller units referred to as microglomeruli, each of which is defined by a distinct set of mitral cells with overlapping, spatially confined dendritic arbors. Neighboring microglomeruli were innervated by distinct subsets of sensory axons and activity was more highly correlated among mitral cells associated with the same microglomerulus than across microglomeruli of the same or different protoglomeruli. These results reveal a precise subcellular organization of the neuropil in the developing OB into distinct processing channels, indicating that the layout of the adult glomerular array is established already early in development.

neuroscience↗

bioRxiv2026

Joint experience-dependent representations of odors and temporal context in the zebrafish homolog of piriform cortex

Intelligent behavior requires experience-dependent internal representations of relevant information. We examined representational learning in telencephalic area pDp of adult zebrafish, the homolog of piriform cortex. Activity was measured by multiphoton imaging through a prism in head-fixed fish trained in an odor discrimination task. Population dynamics could be decomposed into independent modes, each consisting of distributed activity across mixed-selectivity neurons, that encoded odor identity, novelty, and the progression of time. Training sharpened novelty detection, strengthened odor-specific response components, and geometrically untangled representational manifolds. Experience-driven plasticity generalized across chemically related stimuli. Main observations could be reproduced phenomenologically by a model assuming linear interactions between novelty-, identity- and time-encoding activity modes. We conclude that pDp jointly represents olfactory and temporal information relevant for episodic memory. Experience enhances the accessibility and generalization of information, indicating that pDp supports brain-wide learning by geometrically organizing olfactory and time-related information on neural manifolds.

neuroscience↗

bioRxiv2026

Deep anatomical and ultrastructural classification of neurons in the zebrafish olfactory bulb

Neuronal circuits in the olfactory bulb (OB) perform computations fundamental to pattern classification including a decorrelation and normalization of odor-evoked activity. These computations are mediated by diverse interneurons but a comprehensive picture of interneuron types and their microcircuit organization is lacking. We provide a deep anatomical classification of neuron types and their synaptic connectivity in the OB of adult zebrafish, a well-established model to analyze olfactory computations. We reconstructed 459 neurons in an image volume acquired by serial block face scanning electron microscopy and defined 13 neuron classes based on morphological and ultrastructural features. These comprised two classes of projection neurons and 11 interneuron classes, some of which were further separated into subclasses. Ultrastructural information including spine shape, variations in neurite diameter and synaptic arrangements contributed significantly to the distinction of cell types. As in other species, reciprocal synaptic connections were abundant. Targeted synapse annotation revealed systematic connectivity between projection neurons and interneurons. These included microcircuit motifs combining reciprocal and unidirectional connectivity that provide possible structural substrates for gain control and lateral inhibition. The results provide detailed insights into the structural organization of the OB and an anatomical foundation for physiological and computational studies of information processing in olfaction.

neuroscience↗

bioRxiv2026

Odor preference maps to cohesive transcriptional domains in the olfactory bulb

Animals classify odors along behaviorally relevant dimensions, yet how the olfactory bulb, the first stage of olfactory processing, is organized and how this organization relates to odor classification remains unclear. Here, we identify cohesive transcriptional domains within the larval zebrafish olfactory bulb whose differential functional recruitment reflects odor preference. Using a flow-based two-choice assay, we characterized behavioral preferences across chemically diverse odors. Mapping odor-evoked neuronal activity patterns revealed that similarly preferred odors converge onto shared spatial activation patterns in the olfactory bulb. Whole-mount multiplexed spatial transcriptomics revealed that projection neuron and interneuron subtypes form cohesive spatial domains. Mapping odor-activated neurons onto these domains identified preference-biased recruitment of specific neuronal subtypes. Targeted ablation of a TH+ interneuron population associated with attraction selectively impaired attraction while sparing aversion. Together, these findings reveal that the olfactory bulb is organized into spatially cohesive, transcriptionally defined domains and that selective recruitment of specific subtypes is linked to odor preference. Key findingsO_LIChemically diverse odorants elicit behaviors reflecting olfactory preference. C_LIO_LITranscriptionally defined neuronal subtypes form cohesive spatial domains in the olfactory bulb. C_LIO_LIOdorants of opposing preference recruit distinct transcriptional domains in the olfactory bulb. C_LIO_LIA TH+ interneuron population is selectively required for odor attraction but not aversion. C_LI

neuroscience↗

bioRxiv2026

Dynamic cognitive representations in the dorsal pallium of adult zebrafish

Brains rely on internal models of the world to interpret sensory input and to simulate the future. In the mammalian hippocampal-entorhinal network, environments are represented by cognitive maps that contain spatially selective neurons such as place, grid, head direction and object-vector cells1-6. Neurons with allocentric spatial tuning have recently been discovered also in non-mammalian organisms including larval zebrafish7 but it remains unclear to what extent these neurons establish internal cognitive representations. We measured neuronal activity in telencephalic area Dc of head-fixed adult zebrafish exploring a novel, richly structured virtual reality. Neurons were sharply tuned to one or multiple locations and collectively represented environmental space. Activity fields exhibited neuron-specific associations to visual landmarks, indicating a prominent vectorial component in spatial representations. Population activity evolved and became increasingly informative as fish explored the environment. When landmarks were removed after familiarization, landmark-associated activity partially persisted and subsets of neurons reported prediction errors, implying that activity was in part driven by an internal representation. Strong functional coupling among neuronal ensembles and winner-take-all dynamics suggest that representations evolve by refinements of pre-structured networks. The teleost brain therefore generates internal models of structured environments that are optimized by experience and enable cognitive inference and prediction.

neuroscience↗

bioRxiv2024

Representational learning by optimization of neural manifolds in an olfactory memory network

Cognitive brain functions rely on experience-dependent internal representations of relevant information. Such representations are organized by attractor dynamics or other mechanisms that constrain population activity onto "neural manifolds". Quantitative analyses of representational manifolds are complicated by their potentially complex geometry, particularly in the absence of attractor states. Here we trained juvenile and adult zebrafish in an odor discrimination task and measured neuronal population activity to analyze representations of behaviorally relevant odors in telencephalic area pDp, the homolog of piriform cortex. No obvious signatures of attractor dynamics were detected. However, olfactory discrimination training selectively enhanced the separation of neural manifolds representing task-relevant odors from other representations, consistent with predictions of autoassociative network models endowed with precise synaptic balance. Analytical approaches using the framework of manifold capacity revealed multiple geometrical modifications of representational manifolds that supported the classification of task-relevant sensory information. Manifold capacity predicted odor discrimination across individuals better than other descriptors of population activity, indicating a close link between manifold geometry and behavior. Hence, pDp and possibly related recurrent networks store information in the geometry of representational manifolds, resulting in joint sensory and semantic maps that may support distributed learning processes.

neuroscience↗

bioRxiv2024

Computational functions of precisely balanced neuronal assemblies in an olfactory memory network

Structured connectivity in the brain organizes information by constraining neuronal dynamics. Theoretical models predict that memories are represented by balanced assemblies of excitatory and inhibitory neurons, but the existence and functions of such EI assemblies are difficult to explore. We addressed these issues in telencephalic area Dp of adult zebrafish, the homolog of piriform cortex, using computational modeling, population activity measurements, and optogenetic perturbations. Modeling revealed that precise balance of EI assemblies is important to prevent not only excessive firing rates ("runaway activity") but also the stochastic occurrence of high pattern correlations ("runaway correlations"). Consistent with model-derived predictions, runaway correlations emerged in Dp when synaptic balance was perturbed by optogenetic manipulations of fast-spiking feedback interneurons. Moreover, runaway correlations were driven by sparse subsets of strongly active neurons, rather than by a general broadening of tuning curves. These results reveal novel computational functions of EI assemblies in an autoassociative olfactory memory network and support the hypothesis that EI assemblies organize information on continuous representational manifolds rather than discrete attractor landscapes.

neuroscience↗

bioRxiv2023

Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that "focused" activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individuals experience. Such continuous representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.

neuroscience↗

bioRxiv2023

Deep learning-based synaptic event detection

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring synaptic events carry fundamental information about synaptic function and plasticity. However, their stochastic nature and low signal-to-noise ratio present major challenges for the reliable and consistent analysis. Here, we introduce miniML, a supervised deep learning- based method for accurate classification and automated detection of spontaneous synaptic events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event analysis methods in terms of both precision and recall. miniML enables precise detection and quantification of synaptic events in electrophysiological recordings. We demonstrate that the deep learning approach generalizes easily to diverse synaptic preparations, different electrophysiological and optical recording techniques, and across animal species. miniML provides not only a comprehensive and robust framework for automated, reliable, and standardized analysis of synaptic events, but also opens new avenues for high-throughput investigations of neural function and dysfunction.

neuroscience↗

bioRxiv2023

Molecular logic of neuromodulatory systems in the zebrafish telencephalon

The function of neuronal networks is determined not only by synaptic connectivity but also by neuromodulatory systems that broadcast information via distributed connections and volume transmission. To understand the molecular constraints that organize neuromodulatory signaling in the telencephalon of adult zebrafish we used transcriptomics and additional approaches to delineate cell types, to determine their phylogenetic conservation, and to map the expression of marker genes at high granularity. The combinatorial expression of GPCRs and cell type markers indicates that all neuronal cell types are subject to modulation by multiple monoaminergic systems and distinct combinations of neuropeptides. Individual cell types were associated with multiple (typically >30) neuromodulatory signaling networks but expressed only a few diagnostic GPCRs at high levels, suggesting that different neuromodulatory systems act in combination albeit with unequal weights. These results provide a detailed map of cell types and brain areas in the zebrafish telencephalon, identify core components of neuromodulatory networks, highlight the cell type-specificity of neuropeptides and GPCRs, and begin to decipher the logic of combinatorial neuromodulation.

neuroscience↗

bioRxiv2023

The structure and function of neural connectomes are shaped by a small number of design principles

The map of synaptic connectivity among neurons in the brain shapes the computations that neural circuits may perform. Inferring the design principles of neural connectomes is, therefore, fundamental for understanding brain development and architecture, neural computations, learning, and behavior. Here, we learn probabilistic generative models for the connectomes of the olfactory bulb of zebrafish, part of the mouse visual cortex, and of C. elegans. We show that, in all cases, models that rely on a surprisingly small number of simple biological and physical features are highly accurate in replicating a wide range of properties of the measured circuits. Specifically, they accurately predict the existence of individual synapses and their strength, distributions of synaptic indegree and outdegree of the neurons, frequency of sub-network motifs, and more. Furthermore, we simulate synthetic circuits generated by our model for the olfactory bulb of zebrafish and show that they replicate the computation that the real circuit performs in response to olfactory cues. Finally, we show that specific failures of our models reflect missing design features that we uncover by adding latent features to the model. Thus, our results reflect surprisingly simple design principles of real connectomes in three different systems and species, and offer a novel general computational framework for analyzing connectomes and linking structure and function in neural circuits.

neuroscience↗

bioRxiv2021

A viral toolbox for conditional and transneuronal gene expression in zebrafish

The zebrafish is an important model in systems neuroscience but a key limitation is the lack of viral tools to dissect the structure and function of neuronal circuitry. We developed methods for efficient gene transfer and retrograde tracing in adult and larval zebrafish by herpes simplex viruses (HSV1). HSV1 can be combined with the Gal4/UAS system to target cell types with high spatial, temporal and molecular specificity. We also established methods for efficient transneuronal tracing by modified rabies viruses in zebrafish. We demonstrate that HSV1 and rabies viruses can be used to visualize and manipulate genetically or anatomically identified neurons within and across different brain areas of adult and larval zebrafish. An expandable library of viruses is provided to express fluorescent proteins, calcium indicators, optogenetic probes, toxins and other molecular tools. This toolbox creates new opportunities to interrogate neuronal circuits in zebrafish through combinations of genetic and viral approaches.

neuroscience↗

bioRxiv2020

A deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging data

Calcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials ( spikes) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >35 hours from 298 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.

neuroscience↗

bioRxiv2020

Whole-body integration of gene expression and single-cell morphology

Animal bodies are composed of hundreds of cell types that differ in location, morphology, cytoarchitecture, and physiology. This is reflected by cell type-specific transcription factors and downstream effector genes implementing functional specialisation. Here, we establish and explore the link between cell type-specific gene expression and subcellular morphology for the entire body of the marine annelid Platynereis dumerilii. For this, we registered a whole-body cellular expression atlas to a high-resolution electron microscopy dataset, automatically segmented all cell somata and nuclei, and clustered the cells according to gene expression or morphological parameters. We show that collective gene expression most efficiently identifies spatially coherent groups of cells that match anatomical boundaries, which indicates that combinations of regionally expressed transcription factors specify tissue identity. We provide an integrated browser as a Fiji plugin to readily explore, analyse and visualise multimodal datasets with remote on-demand access to all available datasets.

cell biology↗

bioRxiv2019

Predictive neural processing in adult zebrafish depends on shank3b

Intelligent behavior requires a comparison between the predicted and the actual consequences of behavioral actions. According to the theory of predictive processing, this comparison relies on a neuronal error signal that reflects the mismatch between an internal prediction and sensory input. Inappropriate error signals may generate pathological experiences in neuropsychiatric conditions. To examine the processing of sensorimotor prediction errors across different telencephalic brain areas we optically measured neuronal activity in head-fixed, adult zebrafish in a virtual reality. Brief perturbations of visuomotor feedback triggered distinct changes in swimming behavior and different neuronal responses. Neuronal activity reflecting sensorimotor mismatch, rather than sensory input or motor output alone, was prominent throughout multiple forebrain areas. This activity preceded and predicted the transition in motor behavior. Error signals were altered in specific forebrain regions by a mutation in the autism-related gene shank3b. Predictive processing is therefore a widespread phenomenon that may contribute to disease phenotypes.

neuroscience↗

bioRxiv2019

Whitening of odor representations by specific network connectivity

Neuronal computations underlying higher brain functions depend on synaptic interactions among specific neurons. A mechanistic understanding of such computations requires wiring diagrams of neuronal networks. We examined how the olfactory bulb (OB) performs whitening, a fundamental computation that decorrelates activity patterns and supports their classification by memory networks. We measured odor-evoked activity in the OB of a zebrafish larva and subsequently reconstructed the complete wiring diagram by volumetric electron microscopy. The resulting functional connectome revealed an overrepresentation of multisynaptic connectivity motifs that mediate reciprocal inhibition between neurons with similar tuning. This connectivity suppressed redundant responses and was necessary and sufficient to reproduce whitening in simulations. Whitening of odor representations is therefore mediated by higher-order structure in the wiring diagram that is adapted to natural input patterns.

neuroscience↗

bioRxiv2018

Rapid olfactory discrimination learning in adult zebrafish

The zebrafish is a model organism to study olfactory information processing but efficient behavioral procedures to analyze olfactory discrimination and memory are lacking. We devised an automated odor discrimination task for adult zebrafish based on olfactory conditioning of feeding behavior. Presentation of a conditioned odor (CS+), but not a neutral odor (CS-), was followed by food delivery at a specific location. Fish developed differential behavioral responses to CS+ and CS- within a few trials even when odors were similar. The behavioral response to the CS+ was complex and included components reminiscent of food search such as increased swimming speed and water surface sampling. Appetitive behavior was therefore quantified by a composite score that combined measurements of multiple behavioral parameters. Discrimination behavior was robust in different strains and learned preferences could overcome innate odor preferences. These results confirm that zebrafish can rapidly learn to make fine odor discriminations. The procedure is efficient and provides novel opportunities to dissect the neural mechanisms underlying olfactory discrimination and memory.

neuroscience↗