Biology subjects

Davoudi, N.

Publications and source records attributed to Davoudi, N..

3 recordsLinked to original sources

bioRxiv2026

Neuronal selectivity and geometric alignment in the human hippocampus support abstract generalization

Abstract representations allow the brain to extract shared structure across different experiences and generalize knowledge beyond individual situations. Although previous studies have shown that representational geometry plays a critical role in supporting abstraction, it remains unclear how the composition of neuronal populations gives rise to such generalizable representations. Here, we investigated how neuronal selectivity shapes the emergence of abstract representations by combining a controlled computational model with analyses of human hippocampal single-neuron recordings. We first manipulated the composition of artificial neural populations to test whether increasing task-related information alone is sufficient to improve cross-context generalization. Although increasing stimulus- and response-selective neurons enhanced encoding strength, it did not improve generalization across contexts. In contrast, introducing category-selective neurons increased cross-context generalization, demonstrating that the type of information represented by a population is critical for abstraction. Analyses of human hippocampal neurons revealed a similar principle: category-like and identity-like neurons produced comparable increases in stimulus encoding, but category-like neurons produced substantially stronger improvements in cross-context generalization. Further analyses showed that category-like neurons influenced abstraction by reshaping population geometry. Specifically, category-axis alignment across contexts, rather than the strength of category-related separation, was the geometric property most strongly associated with generalization. Mediation analysis further indicated that category-like neurons contribute to abstraction primarily through their ability to increase geometric alignment across contexts. Together, these findings reveal a population-level mechanism linking neuronal selectivity to abstract computation and suggest that flexible generalization depends not simply on increasing neural information, but on organizing information into geometries that preserve task-relevant relationships across changing conditions.

neuroscience↗

bioRxiv2025

Designing a Model to Detect Beta Burst in EEG Using Nonlinear Dynamic Features Based on Machine Learning

BackgroundBeta bursts are brief, transient increases in beta-band (13-30 Hz) EEG activity that play a key role in motor control, particularly in processes like movement initiation and inhibition. While most existing methods detect these bursts using simple amplitude thresholds, they often ignore variability in burst duration and task context. More advanced techniques exist but are computationally demanding, often opaque, and dependent on large datasets and strong modeling assumptions. ObjectivesThis study aims to develop an automated, machine learning-based approach to classify beta bursts using nonlinear dynamic features, considering both burst duration and task condition. MethodEEG data were collected from 26 healthy, right-handed participants during three motor tasks: (1) a right-hand isometric pinch grip at 10% maximum voluntary contraction (MVC), (2) rhythmic right-hand finger opening-closing in response to auditory cues (3-4 seconds), and (3) the same right-hand opening-closing task and concurrent with a steady left-hand isometric pinch grip at 10% MVC. Beta bursts were extracted from the left motor cortex, categorized by duration (short, medium, long), and time-locked to task events. From each burst, four nonlinear features, Fractal Dimension (FD), Wavelet Entropy (WE), Sample Entropy (SE), and Nonlinear Energy Operator (NEO) were calculated to train machine learning (ML) models. ResultsStatistical tests and feature selection revealed that FD, SE, and WE varied significantly with burst duration and task type, while NEO was more limited in sensitivity. ML models trained on these features achieved up to 91.1% validation and 85.7% test accuracy, especially when distinguishing bursts of different durations within the same task. ConclusionsThese findings suggest that beta bursts reflect structured, task-specific neural dynamics rather than random fluctuations. By using nonlinear features and ML, we introduce a scalable, interpretable framework for burst classification that outperforms traditional methods. This approach advances the understanding of transient beta activity and holds promise for real-time neural decoding in neuroscience and neurotechnology.

neuroscience↗

bioRxiv2020

Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions

Computer-aided drug discovery is an essential component of modern drug development. Therein, deep learning has become an important tool for rapid screening of billions of molecules in silico for potential hits containing desired chemical features. Despite its importance, substantial challenges persist in training these models, such as severe class imbalance, high decision thresholds, and lack of ground truth labels in some datasets. In this work we argue in favor of directly optimizing the receiver operating characteristic (ROC) in such cases, due to its robustness to class imbalance, its ability to compromise over different decision thresholds, certain freedom to influence the relative weights in this compromise, fidelity to typical benchmarking measures, and equivalence to positive/unlabeled learning. We also propose new training schemes (coherent mini-batch arrangement, and usage of out-of-batch samples) for cost functions based on the ROC, as well as a cost function based on the logAUC metric that facilitates early enrichment (i.e. improves performance at high decision thresholds, as often desired when synthesizing predicted hit compounds). We demonstrate that these approaches outperform standard deep learning approaches on a series of PubChem high-throughput screening datasets that represent realistic and diverse drug discovery campaigns on major drug target families.

pharmacology and toxicology↗