Software ePosters

Electrophysiological recordings capture signals from hundreds of neurons simultaneously, but isolating single-cell activity often requires manual curation due to limitations in spike- sorting algorithms. As dataset sizes grow, the time and expertise required for accurate and consistent human curation pose a major…

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…

Neural simulations using NEST are typically executed by a Python script that configures the simulator kernel, builds the network, and runs the simulation. The result is a series of files containing the simulated network activity, which can then be analyzed to provide insights into the neural activity. Despite the…

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…

Scientific progress increasingly relies on interdisciplinary collaborations as experimental and simulation datasets grow in complexity. However, these datasets are often underutilized due to inadequate solutions for data management, sharing, and analysis. Existing solutions address different aspects of the data…

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…

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…

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…

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…

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…

The abstract representation of space has been extensively studied in the hippocampus and entorhinal cortex. Yet while a growing variety of theoretical models have been proposed to capture the rich neural and behavioral phenomenology associated with these regions, it remains difficult to compare these theories…

We never stop learning when we suffer, but we all learn in unique ways. Adaptive learning helps us cope with adversities, whereas maladaptive learning could hinder coping and lead to more suffering. Indeed, maladaptive aversive learning is thought to be associated with both mental health disorders and chronic pain.…

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…

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…

Advances in computer vision and deep learning have made it possible to gather markerless pose estimation data from consumer-grade cameras. Accessible software tools, such as DeepLabCut, have made it possible to include behavioral pose as one data modality in larger neuroscience projects. Staying at the leading edge of…