AIMC Topic:
Neurons

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On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation.

IEEE transactions on neural networks and learning systems Aug 13, 2014
This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, th...
Benchmarking Neurons Classification Power Plants Feedback Fuzzy Logic Models, Statistical Algorithms Databases, Factual Reproducibility of Results Neural Networks, Computer Artificial Intelligence Machine Learning
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A hierarchical model of goal directed navigation selects trajectories in a visual environment.

Neurobiology of learning and memory Jul 29, 2014
We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model's fle...
Spatial Navigation Entorhinal Cortex Motion Perception Recognition, Psychology Robotics Goals Neurons Animals Visual Perception Hippocampus Prefrontal Cortex Humans Environment Models, Neurological Neural Networks, Computer Algorithms
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Is extreme learning machine feasible? A theoretical assessment (part II).

IEEE transactions on neural networks and learning systems Jul 23, 2014
An extreme learning machine (ELM) can be regarded as a two-stage feed-forward neural network (FNN) learning system that randomly assigns the connections with and within hidden neurons in the first stage and tunes the connections with output neurons i...
Humans Probability Neural Networks, Computer Artificial Intelligence Algorithms Learning Computer Simulation Models, Neurological Neurons
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Is extreme learning machine feasible? A theoretical assessment (part I).

IEEE transactions on neural networks and learning systems Jul 21, 2014
An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning system whose connections with output neurons are adjustable, while the connections with and within hidden neurons are randomly fixed. Numerous applications have dem...
Learning Models, Neurological Neurons Nerve Net Generalization, Psychological Artificial Intelligence Algorithms Computer Simulation Humans Neural Networks, Computer
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Self-organization of a recurrent network under ongoing synaptic plasticity.

Neural networks : the official journal of the International Neural Network Society Jun 5, 2014
We investigated the organization of a recurrent network under ongoing synaptic plasticity using a model of neural oscillators coupled by dynamic synapses. In this model, the coupling weights changed dynamically, depending on the timing between the os...
Synapses Humans Fourier Analysis Models, Neurological Neural Conduction Action Potentials Neural Networks, Computer Neurons Algorithms Neuronal Plasticity
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Neuronal Waveform Classification in Multielectrode Recordings Using Machine Learning Techniques and Multidimensional Analysis.

International journal of neural systems Jun 1, 2025
Extracellular recordings of neuronal spikes are crucial for studying brain activity. These signals are typically classified based on firing patterns and waveform shape, particularly trough-to-peak duration. While useful, this method oversimplifies th...
Humans Machine Learning Visual Cortex Action Potentials Signal Processing, Computer-Assisted Neurons
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Toward Building Human-Like Sequential Memory Using Brain-Inspired Spiking Neural Models.

IEEE transactions on neural networks and learning systems Jun 1, 2025
The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processe...
Humans Neural Networks, Computer Action Potentials Neurons Brain Memory Computer Simulation Models, Neurological
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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...
Algorithms Models, Neurological Humans Computer Simulation Animals Action Potentials Neural Networks, Computer Feedback Time Factors Machine Learning Neurons Neuronal Plasticity
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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 ...
Humans Neural Networks, Computer Action Potentials Equipment Design Signal Processing, Computer-Assisted Neurons
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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...
Neurons Animals Models, Neurological Action Potentials Humans Neural Networks, Computer
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