AIMC Topic: Neural Networks, Computer

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An Area- and Energy-Efficient Spiking Neural Network With Spike-Time-Dependent Plasticity Realized With SRAM Processing-in-Memory Macro and On-Chip Unsupervised Learning.

IEEE transactions on biomedical circuits and systems
In this article, we present a spiking neural network (SNN) based on both SRAM processing-in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent Plasticity (STDP). Co-design of algorithm and hardware for hardware-friendly SN...

Macroscopic dynamics of neural networks with heterogeneous spiking thresholds.

Physical review. E
Mean-field theory links the physiological properties of individual neurons to the emergent dynamics of neural population activity. These models provide an essential tool for studying brain function at different scales; however, for their application ...

Clarifications regarding convolutional neural networks-based automatic segmentation of pharyngeal airway sections.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets.

Protein science : a publication of the Protein Society
The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target prote...

Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.

Protein science : a publication of the Protein Society
Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statisti...

Analysing cerebrospinal fluid with explainable deep learning: From diagnostics to insights.

Neuropathology and applied neurobiology
AIM: Analysis of cerebrospinal fluid (CSF) is essential for diagnostic workup of patients with neurological diseases and includes differential cell typing. The current gold standard is based on microscopic examination by specialised technicians and n...

A deep learning method to map tissue architecture.

Nature reviews. Genetics
A new study in describes a computational method named UTAG (unsupervised discovery of tissue architecture with graphs) that aims to identify and quantify higher-level tissue domains from biological images without previous knowledge.

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network.

Revista espanola de enfermedades digestivas
BACKGROUND AND AIMS: capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN),...

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.

Toxicological sciences : an official journal of the Society of Toxicology
Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorptio...