AIMC Topic: Neural Networks, Computer

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Distributed Synaptic Connection Strength Changes Dynamics in a Population Firing Rate Model in Response to Continuous External Stimuli.

Neural computation
Neural network complexity allows for diverse neuronal population dynamics and realizes higherorder brain functions such as cognition and memory. Complexity is enhanced through chemical synapses with exponentially decaying conductance and greater vari...

Elucidating the Theoretical Underpinnings of Surrogate Gradient Learning in Spiking Neural Networks.

Neural computation
Training spiking neural networks to approximate universal functions is essential for studying information processing in the brain and for neuromorphic computing. Yet the binary nature of spikes poses a challenge for direct gradient-based training. Su...

The Leaky Integrate-and-Fire Neuron Is a Change-Point Detector for Compound Poisson Processes.

Neural computation
Animal nervous systems can detect changes in their environments within hundredths of a second. They do so by discerning abrupt shifts in sensory neural activity. Many neuroscience studies have employed change-point detection (CPD) algorithms to estim...

Examination of the relationship between D-amino acid profiles and cognitive function in individuals with mild cognitive impairment: a machine learning approach.

The international journal of neuropsychopharmacology
BACKGROUND: The global prevalence of dementia is significantly increasing. Early detection and prevention strategies, particularly for mild cognitive impairment (MCI), are crucial but currently hindered by the lack of established biomarkers. Here, we...

Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.

Nucleic acids research
Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To unearth this vast potential, we present BGC-Prophet, a transformer-based language model for BGC predict...

Analysis of RNA translation with a deep learning architecture provides new insight into translation control.

Nucleic acids research
Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human tran...

Artificial neural networks to predict the presence of Neosporosis in cattle.

Mathematical biosciences and engineering : MBE
The prediction of bovine infectious diseases is a constant challenge as generally, only laboratory data is available not allowing the study of their relationship with each disease's risk factors. The diseases neosporosis and bovine viral diarrhea, wh...

Predicting Body Fat Percentage from Simple Anthropometric Measurements: A Machine Learning Approach.

Studies in health technology and informatics
Accurately assessing body fat percentage (BF%) is crucial for healthcare and fitness but is hindered by gold-standard methods that are costly and invasive. This study employs a dataset containing variables such as age, sex, Body Mass Index (BMI), and...

Assessment of Driver Inattention State Using Multimodal Wearable Signals and Cross-Attention-Driven Hierarchical Fusion.

Studies in health technology and informatics
Identifying driver inattention is crucial for road safety, driver well-being and can be enhanced using multimodal physiological signals. However, effective fusion of multimodal data is highly challenging, particularly with intermediate fusion, where ...

Extracting Regions of Interest and Selective Feature Application in Leukaemia Image Classification.

Studies in health technology and informatics
Evaluating the blood smear test images remains the main route of detecting the type of leukaemia, accurate diagnosis is fundamental in providing effective treatment. The changes in the structure of the white blood cells present different morphologica...