AIMC Topic:
Neurons

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Spike Neural Network of Motor Cortex Model for Arm Reaching Control.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Motor cortex modeling is crucial for understanding movement planning and execution. While interconnected recurrent neural networks have successfully described the dynamics of neural population activity, most existing methods utilize continuous signal...
Nerve Net Movement Motor Cortex Neurons Arm Humans Neural Networks, Computer Animals Models, Neurological Action Potentials
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Recapitulating the electrophysiological features of in vivo biological networks by using a real-time hardware Spiking Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Electroceutical methodologies utilized for treating neurological disorders, including stroke, can leverage neuromorphic engineering principles to design devices capable of seamlessly interfacing with the neural system. This paper introduces a bank of...
Action Potentials Neurons Nerve Net Neural Networks, Computer Animals Rats
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Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks.

Neural computation Jun 7, 2024
The plasticity of the conduction delay between neurons plays a fundamental role in learning temporal features that are essential for processing videos, speech, and many high-level functions. However, the exact underlying mechanisms in the brain for t...
Action Potentials Unsupervised Machine Learning Neurons Brain Neuronal Plasticity Models, Neurological Humans Neural Networks, Computer
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A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models.

Neural computation Jun 7, 2024
Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of ...
Models, Neurological Action Potentials Neurons Computer Simulation Animals Synapses Nerve Net Humans Neural Networks, Computer
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The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex.

Cerebral cortex (New York, N.Y. : 1991) Jun 4, 2024
Neocortex is a complex structure with different cortical sublayers and regions. However, the precise positioning of cortical regions can be challenging due to the absence of distinct landmarks without special preparation. To address this challenge, w...
Deep Learning Imaging, Three-Dimensional Mice Mice, Inbred C57BL Neurons Animals Male Neocortex Tomography, Optical
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Convolutional neural network models applied to neuronal responses in macaque V1 reveal limited nonlinear processing.

Journal of vision Jun 3, 2024
Computational models of the primary visual cortex (V1) have suggested that V1 neurons behave like Gabor filters followed by simple nonlinearities. However, recent work employing convolutional neural network (CNN) models has suggested that V1 relies o...
Neurons Nonlinear Dynamics Photic Stimulation Neural Networks, Computer Animals Visual Cortex Models, Neurological Macaca Primary Visual Cortex
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Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay.

International journal of neural systems Jun 1, 2024
Spiking neural membrane systems (or spiking neural P systems, SNP systems) are a new type of computation model which have attracted the attention of plentiful scholars for parallelism, time encoding, interpretability and extensibility. The original S...
Neural Networks, Computer Time Factors Models, Neurological Humans Synaptic Transmission Neurons Adenosine Triphosphate Synapses Action Potentials Astrocytes
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Approximating Nonlinear Functions With Latent Boundaries in Low-Rank Excitatory-Inhibitory Spiking Networks.

Neural computation Apr 23, 2024
Deep feedforward and recurrent neural networks have become successful functional models of the brain, but they neglect obvious biological details such as spikes and Dale's law. Here we argue that these details are crucial in order to understand how r...
Action Potentials Neurons Nonlinear Dynamics Animals Models, Neurological Humans Synapses Neural Networks, Computer Nerve Net Computer Simulation Neural Inhibition
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A robust balancing mechanism for spiking neural networks.

Chaos (Woodbury, N.Y.) Apr 1, 2024
Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the ...
Synapses Neural Networks, Computer Action Potentials Neuronal Plasticity Neurons Models, Neurological
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ViNe-Seg: deep-learning-assisted segmentation of visible neurons and subsequent analysis embedded in a graphical user interface.

Bioinformatics (Oxford, England) Mar 29, 2024
SUMMARY: Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the...
Image Processing, Computer-Assisted Neurons Animals Software Deep Learning Algorithms User-Computer Interface
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