Biostatistics ePosters

Understanding how neural representations align between biological and artificial systems has emerged as a central challenge in computational neuroscience. While deep neural networks now reliably predict neural responses across multiple brain areas, their utility for understanding biological computation remains limited…

To date, most studies of neural representation have focused on how a single stimulus is encoded in the spiking activity of neurons. Consequently, little is known about how sensory neurons can preserve information from multiple stimuli – a problem given that many sensory neurons have relatively broad receptive fields.…

Dopamine plays a crucial role in the neural mechanisms underlying behavior, yet quantifying its impact through genetic modifications presents significant challenges, particularly due to the high-dimensional nature of the resulting data. In this study, we address these challenges by proposing a novel methodological…

It is appealing to disclose the state of an animal as well as the functional connectivity of its neuronal circuit by calculating pairwise and higher-order interactions (HOI) among neuronal activity. Here, we present a systematic approach to achieve these goals by analysing the spiking activity of simultaneously…

Neuronal tuning changes over time, even in the absence of obvious signs of learning or changes in behaviour. This representational drift causes fixed readouts from a neural population to gradually deteriorate, suggesting that downstream populations adapt to these tuning changes. Many statistical distributions of…

Perception requires integrating noisy dynamic visual information across the visual field to identify relevant stimuli and guide decisions. While temporal integration has been studied extensively in experiments with highly controlled visual stimuli and reverse-correlation techniques, the nonlinear mechanisms underlying…

The analysis of neuronal ensembles often suffers from low spiking activity. As statistical analysis relies on sufficiently large samples, rare joint events in neuronal ensembles usually fail to reach statistical significance. We show that statistical power can be enhanced considerably by careful preselection of…

Automated voltage-clamp recordings from 40 voltage-gated potassium (Kv) channels [1] revealed the inherent kinetic heterogeneity of some Kv types expressed in the brain (e.g. Kv1.3). A model capable of capturing the kinetic Kv heterogeneity requires both fixed model features shared between cells, and the ability to…

Accumulating evidence from electrocorticogram (ECoG) recordings and imaging challenge traditional views of brain oscillations as spatially stationary periodic sources. Understanding the spatio-temporal dynamics of cortical activity requires novel measurement and analysis methodologies. We recorded broadband local…

Information in sensory neurons is conveyed by spiking activity varying in time. This is quantified by the mutual-information rate (MIR), given by $\mathrm{MIR}:=\underset{\Delta t\rightarrow\infty}{\lim}\frac{{\cal I}\left(\Delta t\right)}{\Delta t}$, where ${\cal I}\left(\Delta t\right)$ is the mutual information…

Gut microbiome has strongly emerged as a pivotal factor in the pathogenesis of stroke via gut-brain axis. However, it remains uncertain whether interventions that modulate gut microbiome can constitute a potent strategy to improve stroke outcome. The present study was carried out to explore interventions that affect…

Mild traumatic brain injury (mTBI) poses a significant public health concern due to its association with psychiatric complications, including anxiety, depression, and sleep disturbances. Insulin-like growth factor 1 (IGF-1), known for its implications in acromegaly and cancer, has been linked to metabolic syndrome in…

The analysis of effective connectivity in neuronal networks has become an essential tool for understanding the underlying mechanisms of information transfer in neural circuits. However, its interpretation is not always straightforward, and it can be masked by the presence of artifacts.We scrutinized calcium imaged…

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…

Linear regression and Pearson correlations are bedrock statistical analyses in neuroscience: correlations between neurons are a signature of collective information coding and computation, and linear regression quantifies the linear or nonlinear (e.g., polynomial) relationships among, e.g., neurons or brain regions.…

Both modern and historic neuroscience have been challenged by achieving an understanding of neuron cir-cuits, and determining the computational and organizational principles underlying these circuits. Deeper un-derstanding of the organization of brain circuits and cell types, including in the hippocampus, is required…

Autism Spectrum Disorder (ASD) describes a diverse group of neurodevelopmental disorders encompassing a wide range of clinical impairments. The two core symptoms that define ASD are social communication impairments and restricted and repetitive behaviors, and there is a wide range of cognitive and language abilities.…

To address the question of how to compare DNN model activations to brain data, we investigate what transforms best describe similarity in neural activity in the same brain area between conspecifics. We expect neural responses to be functionally highly similar within a species (since we expect findings to generalize…

In networks of neurons, fine-scale interactions build upon one another to produce large-scale patterns of activity. But inferring these interactions from state-of-the-art experiments poses a fundamental challenge: As the number of cells increases, the number of possible interactions quickly outpaces the number of…

A computation that is thought to underlie many decisions is the gradual accumulation of noisy evidence over time until a bound is reached, which triggers the commitment to a choice. The integration-to-bound framework captures the behavior in many decisions, as well as the pooled responses across neurons in many brain…

Oscillations in neural activity are often studied in signals that reflect electrical currents aggregated over neuronal populations (e.g., local field potentials). Ideally, we could straightforwardly analyze oscillations in individual neurons’ firing, by estimating the power spectral density (PSD) of spike trains.…

The idea of estimating synaptic connectivity and strength from parallel spike trains has been entertained since the late 1960s, but the last few years have seen a resurgence in proposed methods. A majority of model-free techniques estimate synaptic coupling via a “separation of timescales” (SOT) principle applied to…

A common approach to analyzing spike train data in stimulus-response experiments is to estimate spike rates relative to the stimulus onset. These experiments typically involve collecting measurements from the same subject across several trials and sessions, and performing the same experiment across multiple subjects.…

Generalized linear models (GLMs) are a gold-standard in computational neuroscience, allowing for the statistical investigation of neural activity when a population of neurons is exposed to stimuli (Gerwinn, 2010 et al.). Logistic regression models are one of such GLMs that allow for the prediction of two possible…