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Eppler, J.-B.

Publications and source records attributed to Eppler, J.-B..

2 recordsLinked to original sources

Homeostasis of a representational map in the neocortex

Cortical function in general and the processing of sensory stimuli in particular are remarkably robust against the continuous loss of neurons during aging, and even the accelerated loss during prodromal stages of neurodegeneration1,2. Population activity of neurons in sensory cortices represents the environment in form of a map, which is structured in an informative way for guiding behavior. Here, we used the mouse auditory cortex as a model and tested in how far the structure of the representational map is protected by homeostatic network mechanisms against the removal of neurons. We combined longitudinal two-photon calcium imaging of population responses evoked by a diverse set of sound stimuli with a targeted microablation of functionally characterized neurons. Unilateral microablation of 30 - 40 selected highly sound-responsive neurons in layer 2/3 led to a temporary disturbance of the representational map in the spared population that, however, recovered in subsequent days. At the level of individual neurons, we observed that the recovery of the spared network was predominantly driven by neurons unresponsive to the sounds before microablation which strengthened the correlation structure of the local network after gaining responsiveness. In contrast, selective microablation of inhibitory neurons induced a prolonged disturbance of the representational map that was primarily characterized by a destabilization of sound responses across trials. Together, our findings provide a link between the tuning and plasticity of individual neurons and the structure of a representational map at the population level which reveals homeostatic network mechanisms safeguarding sensory processing in neocortical circuits.

neuroscience↗

Fully automated detection of dendritic spines in 3D live cell imaging data using deep convolutional neural networks

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 image simultaneously large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for 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. The core of our pipeline is a deep convolutional neural network, which 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 labelled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection and reaches a near human-level detection performance. 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.

neuroscience↗