The technique of Formal Concept Analysis is applied to a dataset describing the traits of rodents, with the goal of identifying zoonotic disease carriers,or those species carrying infections that can spillover to cause human disease. The concepts identified among these species together provide rules-of-thumb about the …
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13 results for “Rodents”
A generalised framework for detailed classification of swimming paths inside the Morris Water Mazeq-bio.QM
The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents. Over the years, various methods of analysing rodent data collected in this task have been proposed. These methods span from classical performance measurements (e.g. escape latency, rodent speed, quadrant p…
Rodent identifies ODEs from trajectories without needing basis functions.
problem Identifying the generating ODE from observed system trajectories.
method Uses Neural Arithmetic Units and sparsification techniques (VAE and ARD) to minimize state size and non-zero parameters.
result Learned models represent a manifold of ODEs including harmonic signals and Lotka-Volterra systems.
Rodent hippocampal population codes represent important spatial information about the environment during navigation. Several computational methods have been developed to uncover the neural representation of spatial topology embedded in rodent hippocampal ensemble spike activity. Here we extend our previous work and pro…
The paper introduces a new method to measure the shape relations between biological objects using r-parallel sets.
problem The influence of neighboring objects on the shape and function of biological objects.
method The authors develop a theory based on spatial point processes to measure the geometrical interaction between objects.
result The proposed measures provide detailed information about the shape of individual objects and their interactions.
Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imaging assay, a classification model for predicting the biological activity of candidate compounds was introduced. The image recognition model wh…
Six AI solutions accurately detect growth plate planes in mice bone scans.
problem Manual, time-consuming, and variable bone growth plate detection in micro-CT scans.
method Prepared and annotated a dataset of 3D μCT scans, organized a challenge, and developed six computer vision solutions.
result Achieved mean absolute error of 1.91±0.87 planes from ground truth.
Develops a new point process model for detecting neural spike sequences.
problem Detecting sparse sequences of neural spikes in high-dimensional spike trains.
method A point process model that represents sequence occurrences as marked events in continuous time, with learnable time warping parameters.
result Demonstrates improved detection and modeling of neural spike sequences.
New methods improve neural connectivity analysis at submillisecond timescales.
problem Limitations of standard spike train analysis methods in terms of temporal resolution and scalability.
method Developed Monte Carlo and polynomial approximation methods for continuous-time neural spike train analysis.
result Superior accuracy and scalability compared to traditional binned GLMs, enabling precise connectivity inference.
The paper analyzes how grid cells perform path integration and learns hexagon grid patterns.
problem Understanding how grid cells perform path integration calculations.
method Theoretical analysis of a general representation model of path integration by grid cells, identifying group representation and isotropic scaling conditions.
result The learned model of hexagon grid patterns is capable of accurate long distance path integration.
Machine learning detects epilepsy development from EEG before seizures.
problem Early detection of epilepsy development (epileptogenesis) before seizures.
method Deep CNN combined with prediction aggregation for EEG data analysis.
result Deep learning achieves 99% AUC for EPG detection from EEG recordings.
RNNs trained on head direction task mimic brain's compass and shifter neurons.
problem Modeling brain's head direction system using neural networks.
method Optimized recurrent neural networks trained on angular velocity integration.
result RNNs naturally emerge with compass and shifter neuron-like properties.
The computational properties of neural systems are often thought to be implemented in terms of their network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit (MSU) recordings or neuroimaging data, is an important step toward understanding its c…