Python

Build and evaluate deep-learning models for biological prediction using Python and Jupyter notebooks. Lectures and collaborative exercises develop convolutional networks for classification, regression and image segmentation, with attention to preprocessing, augmentation, class imbalance, cross-validation and overfitting. Later sessions explore learning-rate and training-duration choices, recurrent networks and applications to omics data. Participants interpret model performance and discuss their own research problems. 2026-11-23–2026-11-27, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). O

…begins with requests for individual Python functions in chat, then develops multi-step workflows using Claude's desktop tools on local project folders. Participants learn to inspect and correct generated code, document analyses, and distinguish successful execution from scientifically valid methods and assumptions. Genomics and tabular-data exercises cover delegation, review and reproducibility. Live online, 26–27 January, 14:00–18:00 Europe/Berlin (CET). Fee €280. Requires basic Python, a computer o

Analyse droplet-based single-cell RNA-seq data using Python and command-line tools, moving from raw reads through quality control, normalisation, integration and clustering to interpretation. The course covers experimental design, differential expression and abundance, Single Cell Expression Atlas and standards for repository submission. Lectures, computational exercises and a mentored group challenge use virtual machines, Zoom and Slack. Basic Unix/Linux and Python skills are required. Participants should be available 09:00–17:30 UK time each day. There are 35 selected places, at £250 for aca

Develop practical Python skills for collecting, processing and visualising biological data, then train and evaluate machine-learning models using public datasets. Teaching covers APIs, reproducible preprocessing, statistical analysis, neural networks, transformers and potential uses of large language models in life science. Mentored small-group projects apply the methods to biological questions. The course particularly suits PhD students and early-career researchers with beginner Python; previous machine-learning experience is unnecessary. There are 30 selected places, costing £975 for academi

Build Unity interaction and rendering features and a Python language-model backend for the Digital Einstein platform. Evaluate conversational quality and latency, maintain the system and support selected demonstrations; the semester workload is approximately fifteen hours per week.

…The work uses Modelica, Python, optimization and AI, with laboratory or field validation and industrial collaboration. This two-year postdoctoral appointment has monthly pay of USD 8,266–8,921. Priority review is December 30, 2026. Hybrid staff must live within 150 miles of Berkeley Lab.

Recent large-scale community effort has produced comprehensive mapping of neurons and chemical synapses in both the Drosophila larva and adult brains (‘connectomes’). In addition, the neurotransmitter expression of each neuron can be predicted using a machine learning algorithm. However, interpreting connectomics data has proven challenging: most neurons connect to tens/hundreds of upstream/downstream neurons, and removing weak connections eliminates most connections in the brain. This could lead to biassed usage of connectomics data, favouring neurons previously studied, or those accessible w

Investigating the contribution of various neuronal circuits to our information processing, learning, and actions is often limited by the difficulty of providing precise and scalable complex experiences with detailed behavior measurements.Despite recent advances, existing behavior platforms are typically optimized for few, often simple tasks with constrained extendability, and priced prohibitively for scale, while behavior tracking requires work intensive labeling to generate situation-specific training data.Here, we present work on two novel open and flexible tools to enable easier pursuit of

Breaking the neural code requires the systematic identification and characterization of neuronal ensembles related to specific brain functions. Recent research suggests that neuronal ensembles underlie brain functions such as perception, movement, and memory processes. However, the standardization of different mathematical algorithms that allow the identification and characterization of the properties of neuronal ensembles is still lacking in the systems neuroscience field. Our work addresses this gap by introducing an open-source Graphic User Interface (GUI) designed for researchers intereste

…leverages the strength of Houdini and Python software. This approach combines industry-standard graphic libraries with a dynamic user interface. Anatomists can interactively visualise, slice, annotate and analyse brain regions with high reproducibility. In Houdini, readability and traceability are ensured by simultaneously displaying results as a 3D ob

Quantifying the number and strength of synapses, and how these change over time is critical to understanding neuronal activity and plasticity. The dynamics of dendritic spines and the molecular composition of dendrites is strongly dependent on image analysis that can detect the position and quantity of labelled proteins and mRNAs of interest. Usually, light microscopy images are analyzed manually and involves switching between multiple software packages to locate spines, track dendrites, or identify mRNA puncta for each process step. State-of-the-art automated tools, while powerful, can often

…these challenges, we developed a Python-based workflow with a graphical user interface using PyQt5. It uses a modular fra

…from high-level model definitions in Python. Here, we present Brian2CUDA, an open-source software that extends the Brian simul

Recent advances in neuroscience technology enable experiments on exciting new spatial and temporal scales, creating opportunities for novel paradigms such as closed-loop optogenetic control of neural activity on the order of milliseconds. At the same time, computational models are becoming ever more powerful for reproducing behavior and neural activity. The complexities of these advances, however, can make it difficult to reap the benefits of bridging model and experiment, such as accelerating experiment development with in-silico prototyping or validating the output of a simulation against ex

…means that users write their code as a Python script, with textual representations of the model equations, and the simulator transparently translates this code into a target language such as C++. In this talk, I will give an overview over recent developments in the Brian ecosystem, showcasing a number of packages that enhance the simulator's capabili

…Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-

…Viejo will present Pynapple, a Python-based data analysis pipeline for systems neuroscience. Designed for flexibility and versatility, Pynapple allows users to perform cross-

…You should be comfortable using Python, Jupyter notebooks, Google Colab, Numpy and, preferably, object oriented programming.

Autopilot is a Python framework for performing complex behavioral neuroscience experiments by coordinating a swarm of Raspberry Pis. It was designed to not only give researchers a tool that allows them to perform the hardware-intensive experiments necessary for the next generation of naturalistic neuroscientific observation, but also to make it easier for scientists to be good stewards of the human knowledge project. Specifically, we designed Autopilot as a framework that lets its users contribute their technical expertise to a cumulative library of hardware interfaces and experimental designs

…we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and

…initiative aimed at developing common Python tools for computational neuroanatomy. These include cellfinder for fast, accurate cell detection in whole-brain microscopy images, brainreg for aligning images t