Software Development ePosters

To study how motor circuits control bodies in robust, adaptive, and flexible ways, we often break down the complex control system into individual components to be studied in isolation. However, it is increasingly recognized that we need to consider biomechanically realistic models when interpreting the neural…

The study of animal locomotion and neuromechanical control offers valuable insights for advancing research in neuroscience, biomechanics, and robotics. We have developed FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary framework, designed to facilitate access to…

Advancements in machine learning have drastically improved our ability to fit biophysical models of neural systems. We present here a fully-differentiable, biophysical model of the mouse retina that is primed for gradient-based optimization. Our model is based on the Jaxley software library (Deistler et al. 2024,…

The rise of spiking neural networks (SNNs) offers a promising alternative to traditional activation-based deep neural networks, advancing the pursuit of more sustainable and ethical computing architectures. Despite its potential, neuromorphic event-based computing, which promises to fully harness the benefits of…

NEST [1] is a well-established open source simulator providing researchers in computational neuroscience with the ability to perform high-performance simulations of spiking neuronal networks. However, as the simulation kernel is written in C++ for performance reasons, this makes it challenging for researchers without…

Arbor is a software library that serves to efficiently simulate large-scale networks of neurons with detailed morphological structure. It combines facilitated customization of neuronal and synaptic mechanisms with high-performance computing, enabling to use diverse backend architectures such as multi-core CPU and GPU…

Background: Biophysical network modeling (BNM) of the brain is a promising technique for bridging macro- and microscale levels of investigation, enabling inferences about latent features of brain activity such as excitation-inhibition balance. This approach allows personalized models of the brain to be fitted to…

Even with complete knowledge of the underlying wiring diagram using electron microscopy and the neuronal activity through $in~vivo$ recordings, we can only ever correlate neuron function with participation in the various non-random motifs that have been characterized [1, 2]. Causation, however, can only be proven by…

Biophysical neuron models provide mechanistic insight about empirically observed phenomena. However, biophysical neuron models are expensive to simulate, thereby limiting the scale of biophysical models, and optimizing their parameters is notoriously difficult, thereby preventing the fitting of biophysical models to…

A growing number of neuroimaging tools are being developed to enable data analysis in a more tool-independent manner when analyzing three- and four-dimensional brain imaging data in various statistical settings. Ideally, such tools still offer user interfaces that are accessible to those with a basic understanding of…

Much of our current understanding of brain-based visual processing is derived from studies involving immobilised animals. However, in reallife scenarios, visual perception is intrinsically linked with the movements of the eyes, head, and body. To gain amore comprehensive understanding of visual processing under these…

Investigations of transcriptomic and neuroimaging data imply that the spatial patterning of the brain transcriptome follows broad spatial trends in the brain network architecture, which can be used to study the molecular mechanisms of functional connectivity. However, the sophisticated pre-processing and data fusion…

Brain atlases like the Allen Mouse Brain Common Coordinate Framework (CCFv3) are essential tools to guide the integration of different sources of neuroscience data. In this study, we introduce a novel method for curating brain atlas regions that leverages the strength of Houdini and Python software. This approach…

Accurate localization of EEG scalp electrodes is crucial for precisely estimating the underlying neuronal activity from observed electrical potential changes on the scalp. Various techniques for electrode localization have been proposed, categorized into direct measurements on the subject's head or indirect methods…

Neurons rely on the interplay between dendritic morphology and ion channels to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental for exploring this interaction. However, such models can be challenging to understand and validate due to…

Joint exploration of behavior, genes and brain structure holds the promise to provide a better understanding of neural circuits than unimodal data analyses. Several consortia provide extensive data resources of different modalities. However, analyses of heteromodal data require the expertise of computer scientists for…

Processing of large amounts of miniscopic data often poses a time-keeping procedure, requiring user’s efforts for step-by-step launching of various procedures, including manual intervention and inspection of putative neural units. The aim of this NoRMCorre- and CaImAn-based full-Python pipeline is to optimize users'…

Quantitative microscopy workflows have evolved dramatically over the last years, progressively becoming more complex with the emergence of deep learning. Long-standing challenges like 3D segmentation of dense microscopy data can finally be addressed, and new imaging modalities are breaking records in both resolution…

Although automated systems for rodent behavior annotation exist, specific behaviors are still scored by hand. We introduce a novel AI-assisted annotation tool, SmartAnnotator, which helps the researcher annotate behaviors through active learning. Active learning reduces annotation effort by automatically selecting…

EEG is widely used as an efficient tool in clinical diagnosis and human cognitive studies due to its high temporal resolution. While EEG provides a wealth of physiological information about the brain, the execution of data analysis algorithms is rather time-consuming due to the massive amount of EEG data and the…

Recent studies have shown that behavioral information is richly distributed across the brain, but how individual neurons across the brain encode behavior is largely unknown. However, it is challenging to record/interpret high quality single neuron traces across the brain. Here, we describe new hardware/software and…

Computer vision algorithms now allow neuroscientists to quantify animal movement in unprecedented detail, shedding new light on problems in motor control, social behavior, navigation, and beyond. Existing models are only trained with labeled frames (supervised learning). Although effective in many cases, the…

The Brian simulator, a simulator for spiking neural networks, started its life in 2007 and has been continuously developed ever since. A complete rewrite in 2014 led to the "brian2" package, which is now built as a code generation framework. This means that users write their code as a Python script, with textual…

Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used bioinformatics method that defines gene clusters (modules) based on correlated expression levels, allowing users to study relationships between modules, define groups of genes subserving common biological functions, and compare network topologies…