AIMC Topic: Time Factors

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Replay as a Basis for Backpropagation Through Time in the Brain.

Neural computation
How episodic memories are formed in the brain is a continuing puzzle for the neuroscience community. The brain areas that are critical for episodic learning (e.g., the hippocampus) are characterized by recurrent connectivity and generate frequent off...

Role of short-term plasticity and slow temporal dynamics in enhancing time series prediction with a brain-inspired recurrent neural network.

Chaos (Woodbury, N.Y.)
Typical reservoir networks are based on random connectivity patterns that differ from brain circuits in two important ways. First, traditional reservoir networks lack synaptic plasticity among recurrent units, whereas cortical networks exhibit plasti...

Network Delay Forecast and Master-Slave Consistency Enhancement for Remote Surgical Robots.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The inevitable network delay can directly impact the process of remote surgeries and affect the master-slave motion consistency, and sudden changes in delay can compromise surgical safety.

Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1-Weighted Pediatric Brain MRI Using Deep Learning-Based Reconstruction.

Korean journal of radiology
OBJECTIVE: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality.

Ambient artificial intelligence scribes: utilization and impact on documentation time.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.

Stratification of Early Arrhythmic Risk in Patients Admitted for Acute Coronary Syndrome: The Role of the Machine Learning-Derived "PRAISE Score".

Clinical cardiology
BACKGROUND: The PRAISE (PRedicting with Artificial Intelligence riSk aftEr acute coronary syndrome) score is a machine learning-based model for predicting 1-year adverse cardiovascular or bleeding events in patients with acute coronary syndrome (ACS)...

Trainable Reference Spikes Improve Temporal Information Processing of SNNs With Supervised Learning.

Neural computation
Spiking neural networks (SNNs) are the next-generation neural networks composed of biologically plausible neurons that communicate through trains of spikes. By modifying the plastic parameters of SNNs, including weights and time delays, SNNs can be t...

Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.

Radiology. Artificial intelligence
The diagnostic performance of an artificial intelligence (AI) clinical decision support solution for acute intracranial hemorrhage (ICH) detection was assessed in a large teleradiology practice. The impact on radiologist read times and system efficie...

Evaluating the consistency of lenition measures: Neural networks' posterior probability, intensity velocity, and duration.

The Journal of the Acoustical Society of America
Predictions of gradient degree of lenition of voiceless and voiced stops in a corpus of Argentine Spanish are evaluated using three acoustic measures (minimum and maximum intensity velocity and duration) and two recurrent neural network (Phonet) meas...