AIMC Topic: Neurons

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A Heterogeneously Integrated Spiking Neuron Array for Multimode-Fused Perception and Object Classification.

Advanced materials (Deerfield Beach, Fla.)
Multimode-fused sensing in the somatosensory system helps people obtain comprehensive object properties and make accurate judgments. However, building such multisensory systems with conventional metal-oxide-semiconductor technology presents serious d...

A neuroscience-inspired spiking neural network for EEG-based auditory spatial attention detection.

Neural networks : the official journal of the International Neural Network Society
Recent studies have shown that alpha oscillations (8-13 Hz) enable the decoding of auditory spatial attention. Inspired by sparse coding in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The prop...

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis.

Advanced materials (Deerfield Beach, Fla.)
Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning ...

Classification of antiseizure drugs in cultured neuronal networks using multielectrode arrays and unsupervised learning.

Epilepsia
OBJECTIVE: Antiseizure drugs (ASDs) modulate synaptic and ion channel function to prevent abnormal hypersynchronous or excitatory activity arising in neuronal networks, but the relationship between ASDs with respect to their impact on network activit...

Normalized unitary synaptic signaling of the hippocampus and entorhinal cortex predicted by deep learning of experimental recordings.

Communications biology
Biologically realistic computer simulations of neuronal circuits require systematic data-driven modeling of neuron type-specific synaptic activity. However, limited experimental yield, heterogeneous recordings conditions, and ambiguous neuronal ident...

Multi-state MRAM cells for hardware neuromorphic computing.

Scientific reports
Magnetic tunnel junctions (MTJ) have been successfully applied in various sensing application and digital information storage technologies. Currently, a number of new potential applications of MTJs are being actively studied, including high-frequency...

Multisample Online Learning for Probabilistic Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) capture some of the efficiency of biological brains for inference and learning via the dynamic, online, and event-driven processing of binary time series. Most existing learning algorithms for SNNs are based on determin...

Deep-Learning-Based Automated Neuron Reconstruction From 3D Microscopy Images Using Synthetic Training Images.

IEEE transactions on medical imaging
Digital reconstruction of neuronal structures from 3D microscopy images is critical for the quantitative investigation of brain circuits and functions. It is a challenging task that would greatly benefit from automatic neuron reconstruction methods. ...

Robust Transcoding Sensory Information With Neural Spikes.

IEEE transactions on neural networks and learning systems
Neural coding, including encoding and decoding, is one of the key problems in neuroscience for understanding how the brain uses neural signals to relate sensory perception and motor behaviors with neural systems. However, most of the existed studies ...

Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit information, which are not only biologically realistic but also suitable for ultralow-power event-driven neuromorphic implementation. Just like other deep lear...