Biology subjects

Gerhard, S.

Publications and source records attributed to Gerhard, S..

9 recordsLinked to original sources

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↗

bioRxiv2025

Sexually-dimorphic neurons in the Drosophila whole-brain connectome

Sexual dimorphisms in the brain arise from differences in cell number, morphology, and connectivity, and underlie sex-specific behaviors. In Drosophila, these differences are largely governed by the transcription factors Fruitless (Fru) and Doublesex (Dsx), expressed in neurons implicated in reproductive and social behaviors. However, how these neurons are integrated into the connectome to drive different behaviors in different sexes remains unclear. Here, we identify 98 putative Fru- or Dsx-expressing cell types ([~]1700 neurons) in the female whole-brain connectome by matching electron microscopy reconstructions to light microscopy images. We then analyze their connectivity within the whole-brain network. Fru/Dsx neurons are strongly interconnected, but are not part of a private network. We measure distances to the sensory periphery to examine multisensory interactions, and map connections to descending neurons that drive behavior. We further cluster Fru/Dsx neurons based on whole-brain connectivity, and uncover functional groupings. We analyze a subset of strongly connected Fru/Dsx neurons to make functional predictions, and test some of these with experiments. Together, this work provides a brain-wide framework for understanding the organization of sexually dimorphic circuits and establishes a resource for dissecting the neural basis of social and sexual behaviors.

neuroscience↗

bioRxiv2025

Visual pathway origins: an electron microscopic volume for connectomic analysis of the human foveal retina

The fovea is a unique specialization of the primate retina and is a promising site for obtaining the first complete connectome of a human central nervous system (CNS) structure. Within the fovea, neural cells and circuits have been miniaturized and compressed during evolution to sample the visual image at highest spatial resolution and begin the neural processing that serves human form, color, and motion perception. Here we present a comprehensive analysis of a sample of human foveal retina using deep learning-based segmentation to reconstruct all cells and synaptic connections at nanoscale resolution. We classified [~]3,000 cells into 51 distinct morphological types based on their structural features and connectivity patterns. Our observations reveal novel synaptic pathways absent in non-human primates, suggesting specialized circuits contribute uniquely to human trichromatic color vision. A biophysical model of the distinct connectomes made by gap junctions (electrical synapses) between short- (S) and medium-long- (ML) wavelength-sensitive cone photoreceptors, suggests chromatic interactions between S and ML cones prior to the first chemical synapse. Segmentation of retinal ganglion cells (RGCs) suggests the presence of only 11 visual pathways, with 5 high-density RGC pathways accounting for over 95% of foveal output to the brain: a dramatic contrast to the 40+ ganglion cell types recognized in mouse retina. Our connectomic analysis reveals distinctive features of human neural circuitry and demonstrates how AI-based computational approaches can advance understanding of human brain structure and function. Significant statementVision begins in the retina, an accessible outpost of the brain located at the back of the eye. When we directly view an object, we use the fovea--a tiny region containing over 50 distinct, densely packed neuron types. How these diverse cells interconnect through synapses to transmit visual information has been difficult to determine. Here, we used artificial intelligence-based computational methods to create a complete wiring diagram--a connectome--mapping every neuron and synapse in a sample of human fovea. This connectomic resource will be freely available to researchers worldwide. Discoveries so far include synaptic circuits linked to human color vision, demonstrating how connectomics can reveal what makes the human nervous system unique.

neuroscience↗

bioRxiv2023

Synaptic architecture of leg and wing motor control networks in Drosophila

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

neuroscience↗

bioRxiv2022

Tools for comprehensive reconstruction and analysis of Drosophila motor circuits

Like the vertebrate spinal cord, the insect ventral nerve cord (VNC) mediates limb sensation and motor control. Here, we apply automated tools for electron microscopy volume alignment, neuron segmentation, and synapse prediction toward creating a connectome of an adult female Drosophila VNC. To interpret a connectome, it is crucial to know its relationship with the rest of the body. We therefore mapped the muscle targets of leg and wing motor neurons in the connectome by comparing their morphology to genetic driver lines, dye fills, and X-ray nano-tomography of the fly leg and wing. Knowing the outputs of the connectome allowed us to identify neural circuits that coordinate the wings and legs during escape takeoff. We provide the reconstruction of VNC circuits and motor neuron atlas, along with tools for programmatic and interactive access, as community resources to support experimental and theoretical studies of how the fly nervous system controls behavior.

neuroscience↗

bioRxiv2020

Time-controlled Multichannel Dynamic Traction Imaging of Biaxially Stretched Adherent Cells

Here, a dynamic traction force microscopy method is described which enables sub-second temporal resolution imaging of transient subcellular events secondary to extrinsic stretch of adherent single cells. The system employs a novel tracking approach with minimal computational overhead to compensate substrate-based stretch-induced motion/drift of stretched single cells in real time, allowing capture of biophysical phenomena on multiple channels by fluorescent multichannel imaging on a single camera, thus avoiding the need for beam splitting with associated loss of light. The potential impact of the technique is demonstrated by characterizing transient subcellular forces and corresponding nuclear deformations in equibiaxial stretching experiments, uncovering a high frequency strain-rate dependent response in the transfer of substrate strains to the nucleus.

cell biology↗

bioRxiv2019

Automatic Detection of Synaptic Partners in a Whole-Brain Drosophila EM Dataset

The study of neural circuits requires the reconstruction of neurons and the identification of synaptic connections between them. To scale the reconstruction to the size of whole-brain datasets, semi-automatic methods are needed to solve those tasks. Here, we present an automatic method for synaptic partner identification in insect brains, which uses convolutional neural networks to identify post-synaptic sites and their pre-synaptic partners. The networks can be trained from human generated point annotations alone and require only simple post-processing to obtain final predictions. We used our method to extract 244 million putative synaptic partners in the fifty-teravoxel full adult fly brain (FAFB) electron microscopy (EM) dataset and evaluated its accuracy on 146,643 synapses from 702 neurons with a total cable length of 312 mm in four different brain regions. The predicted synaptic connections can be used together with a neuron segmentation to infer a connectivity graph with high accuracy: between 92% and 96% of edges linking connected neurons are correctly classified as weakly connected (less than five synapses) and strongly connected (at least five synapses). Our synaptic partner predictions for the FAFB dataset are publicly available, together with a query library allowing automatic retrieval of up- and downstream neurons.

neuroscience↗

bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

neuroscience↗

bioRxiv2017

Conserved Neural Circuit Structure Across Drosophila Larva Development Revealed By Comparative Connectomics

Throughout an animals postembryonic development, neuronal circuits must maintain appropriate output even as the body grows. The contribution of structural adaptation -- neuronal morphology and synaptic connectivity -- to circuit development remains unclear. In a previous paper (Schneider-Mizell et al., 2016), we measured the detailed neuronal morphological structures subserving neuronal connectivity in Drosophila. Here, we examine how neuronal morphology and connectivity change across postembyronic development. Using new and existing serial section electron microscopy volumes, we reconstructed an identified nociceptive circuit in two larvae, one 1st instar and one 3rd instar. We found extremely consistent, topographically-arranged circuit structure. Five-fold increases in size of interneurons were associated with compensatory structural changes that maintained cell-type-specific synaptic input as a fraction of total inputs. An increase in number of synaptic contacts was accompanied with a disproportionate increase in the number of small dendritic terminal branches relative to other neuronal compartments. We propose that these precise patterns of structural growth act to conserve the computational function of a circuit, for example determining the location of a nociceptive stimulus.

neuroscience↗