Neuro-Informatics ePosters

We present, for the first time, insights into functional synchronization of individual neurons under a complete anatomical neural connectivity map at a massive scale. While functional synchrony has been extensively studied as a mechanism of information processing in the brain, previous work has been limited to small…

We present a comprehensive dataset of neural activity, compiled from 11 neuroimaging experiments in the nematode worm C. elegans. These source datasets, while collected under different experimental protocols, all measure neural activity via changes in calcium fluorescence in labeled subsets of the hermaphrodite worm’s…

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…

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…

Recent studies have demonstrated that brain areas involved in motor control display common representations of latent trajectories for both stereotypical and generalizable dynamical cortical behavior. However, current research often focuses on analyzing single tasks and individual sessions, missing the opportunity to…

Understanding how neuronal populations interact to process information and generate behaviour is a central goal of neuroscience. This can be challenging due to the high dimensionality of the neuronal populations, the dense interactions between them and the unobserved factors that influence the dynamics. The neuronal…

Since the identification of concept cells nearly two decades ago [1], the semantic invariance of single neurons in the human medial temporal lobe has been extensively investigated [2, 3]. Such investigations typically consist of an initial screening to identify semantically-tuned neurons and a subsequent follow-up…

Reliably identifying cell types is crucial for understanding neural systems (1). However, classifying the neurons recorded in large-scale population recordings into well-defined types remains a challenge. While some methods rely on genetic targeting or anatomy for classification, a functional approach used in sensory…

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii)…

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…

Ongoing neural activity fluctuates over a broad range of timescales. Variations in intrinsic timescales across the forebrain relate to the functional specialization of cortical areas along the visual hierarchy [1, 2]. Furthermore, these timescales adapt to task demands, for example, during selective attention [3] and…

Understanding how tasks are processed across the entire brain is a central yet complex question in neuroscience. Recently, the release of brainwide electrophysiological recordings in a standardized behavior task offers an unprecedented opportunity to approach this question. In this paper, we characterize the signal…

Glial cells show great diversity and differ in terms of their functional roles. Microglia, oligodendrocytes, and astrocytes interact with neurons and thus ensure the functionality of the neurons. However, little is known about how they interact with different neuronal cell types. We investigate glial and neuronal…

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…

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…

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'…

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…

A common way to advance our understanding of brain processing is to decode behavior from recorded neural signals. In order to study the neural correlates of learning a task, we would like to decode behavior across the entire timespan of learning, which can take multiple recording sessions across many days. However,…

Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest in modeling neural dynamics during adaptive behaviors to probe neural representations. In particular, neural latent embeddings can reveal…

In this study, we aim to broaden our understanding of the mouse’s primary visual cortex by modeling data provided by the datasets from Stringer et al. (2019) and the Allen Institute. We investigated the roles of the different excitatory and inhibitory cell types in the visual cortex whose functions remain unclear.…

Graph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes -- in particular, to find pairings of neurons across hemispheres. However, since graph matching techniques deal with two isolated…