AIMC Topic: Neurons
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STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks.
IEEE transactions on neural networks and learning systems
Jun 1, 2025
Spiking neural networks (SNNs) are biologically plausible models known for their computational efficiency. A significant advantage of SNNs lies in the binary information transmission through spike trains, eliminating the need for multiplication opera...
NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network With a Diamond Topology for Real-Time Data Processing.
IEEE transactions on biomedical circuits and systems
Jun 1, 2025
The realization of brain-scale spiking neural networks (SNNs) is impeded by power constraints and low integration density. To address these challenges, multi-core SNNs are utilized to emulate numerous neurons with high energy efficiency, where spike ...
Unsupervised post-training learning in spiking neural networks.
Scientific reports
May 21, 2025
The human brain is a dynamic system that is constantly learning. It employs a combination of various learning strategies to facilitate complex learning processes. However, implementing biological learning mechanisms into Spiking Neural Networks (SNNs...
A multiscale brain emulation-based artificial intelligence framework for dynamic environments.
Scientific reports
May 21, 2025
Achieving general artificial intelligence (AGI) has long been a grand challenge in the field of AI, and brain-inspired computing is widely acknowledged as one of the most promising approaches to realize this goal. This paper introduces a novel brain-...
Single-cell mitochondrial morphomics reveals cellular heterogeneity and predicts complex I, III, and ATP synthase Inhibition responses.
Scientific reports
May 14, 2025
Mitochondrial heterogeneity drives diverse cellular responses in neurodegenerative diseases, complicating the evaluation of mitochondrial dysfunction. In this study, we describe a high-throughput imaging and analysis approach to investigate cell-to-c...
Virtual white matter: a novel system for cross-dish neural interaction and modulation.
Journal of neural engineering
May 14, 2025
. Biological neural networks (BNNs) are characterized by complex interregional connectivity, allowing for seamless communication between different brain regions.models traditionally consist of single-dish neural cultures that cannot recapitulate the ...
Neural Code Translation With LIF Neuron Microcircuits.
Neural computation
May 14, 2025
Spiking neural networks (SNNs) provide an energy-efficient alternative to traditional artificial neural networks, leveraging diverse neural encoding schemes such as rate, time-to-first-spike (TTFS), and population-based binary codes. Each encoding me...
Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks.
Neural computation
May 14, 2025
The study of brain activity spans diverse scales and levels of description and requires the development of computational models alongside experimental investigations to explore integrations across scales. The high dimensionality of spiking networks p...
Dynamics of Continuous Attractor Neural Networks With Spike Frequency Adaptation.
Neural computation
May 14, 2025
Attractor neural networks consider that neural information is stored as stationary states of a dynamical system formed by a large number of interconnected neurons. The attractor property empowers a neural system to encode information robustly, but it...
ASiDentify (ASiD): a machine learning model to predict new autism spectrum disorder risk genes.
Genetics
May 8, 2025
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects nearly 3% of children and has a strong genetic component. While hundreds of ASD risk genes have been identified through sequencing studies, the genetic heterogeneity of ASD ...
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