Biology subjects

Muller, K. R.

Publications and source records attributed to Muller, K. R..

3 recordsLinked to original sources

bioRxiv2025

Parameterization of intraoperative human microelectroderecordings: Linking action potential morphology to brainanatomy

Deep brain stimulation (DBS) is a targeted manipulation of brain circuitry to treat neurological and neuropsychiatric conditions. Optimal DBS lead placement is essential for treatment efficacy. Current targeting practice is based on preoperative and intraoperative brain imaging, intraoperative electrophysiology, and stimulation mapping. Electrophysiological mapping using extracellular microelectrode recordings aids in identifying functional subdomains, anatomical boundaries, and disease-correlated physiology. The shape of single-unit action potentials may differ due to different biophysical properties between cell-types and brain regions. Here, we describe a technique to parameterize the structure and duration of sorted spike units using a novel algorithmic approach based on canonical response parameterization, and illustrate how it may be used on DBS microelectrode recordings. Isolated spike shapes are parameterized then compared using a spike similarity metric and grouped by hierarchical clustering. When spike morphology is associated with anatomy, we find regional clustering in the human globus pallidus. This method is widely applicable for spike removal and single-unit characterization and could be integrated into intraoperative array-based technologies to enhance targeting and clinical outcomes in DBS lead placement. Author summaryWe developed a new algorithmic approach to capture the structure of single neuron electrophysiology from microelectrode recordings during deep brain stimulation surgery. This method has broad applications for spike characterization, clustering, removal from the background field potential, and comparison across recording sites. Using this technique, we illustrate regional clustering of spike morphologies in the human globus pallidus.

neuroscience↗

bioRxiv2024

Modeling Attention and Binding in the Brain through Bidirectional Recurrent Gating

Attention is a key component of the visual system, essential for perception, learning, and memory. Attention can also be seen as a solution to the binding problem: concurrent attention to all parts of an entity allows separating it from the rest. However, the rich models of attention in computational neuroscience are generally not scaled to real-world problems and there are thus many behavioral and neural phenomena that current models cannot explain. Here, we propose a bidirectional recurrent model of attention that is inspired by the emerging understanding of biological object-based attention and modern neural networks for image segmentation. It conceptualizes recurrent connections as a multi-stage internal gating process where bottom-up connections transmit features, while top-down and lateral connections transmit attentional gating signals. Our model can recognize and segment simple stimuli such as digits as well as objects in natural images and is able to be prompted with object labels, attributes or locations. It can learn to perform a range of behavioral findings, such as object binding, selective attention, inhibition of return, and visual search. It also replicates a variety of neural findings, including increased activity for attended objects, features, and locations, attention-invariant tuning, and relatively late onset attention. Most importantly, our proposed model unifies decades of cognitive and neurophysiological findings of visual attention into a single principled architecture. Our results highlight that the ability to selectively and dynamically focus on specific parts of stimulus streams can help artificial neural networks to better generalize and align with human brains.

neuroscience↗

bioRxiv2020

DeepCOMBI: Explainable artificial intelligence for the analysis and discovery in genome-wide association studies

Deep learning algorithms have revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence (XAI) has emerged as a novel area of research that goes beyond pure prediction improvement. Knowledge embodied in deep learning methodologies is extracted by interpreting their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called DeepCOMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers decisions by applying layerwise relevance propagation as one example from the pool of XAI techniques. The resulting importance scores are eventually used to determine a subset of most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 WTCCC study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw p-value thresholding as well as other baseline methods. Moreover, two novel disease associations (rs10889923 for hypertension and rs4769283 for type 1 diabetes) were identified.

bioinformatics↗