AIMC Topic: Neurons

Clear Filters Showing 311 to 320 of 1455 articles

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics.

Scientific reports
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-sca...

Mode combinability: Exploring convex combinations of permutation aligned models.

Neural networks : the official journal of the International Neural Network Society
We explore element-wise convex combinations of two permutation-aligned neural network parameter vectors Θ and Θ of size d. We conduct extensive experiments by examining various distributions of such model combinations parametrized by elements of the ...

A topological deep learning framework for neural spike decoding.

Biophysical journal
The brain's spatial orientation system uses different neuron ensembles to aid in environment-based navigation. Two of the ways brains encode spatial information are through head direction cells and grid cells. Brains use head direction cells to deter...

Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Modeling neuron responses to stimuli can shed light on next-generation technologies such as brain-chip interfaces. Furthermore, high-performing models can serve to help formulate hypotheses and reveal the mechanisms underlying neural responses. Here ...

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.

Scientific data
Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompasses three i...

Inference of network connectivity from temporally binned spike trains.

Journal of neuroscience methods
BACKGROUND: Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, meth...

Large-Scale Bio-Inspired FPGA Models for Path Planning.

IEEE transactions on biomedical circuits and systems
The hippocampus provides significant inspiration for spatial navigation and memory in both humans and animals. Constructing large-scale spiking neural network (SNN) models based on the biological neural systems is an important approach to comprehend ...

Scalable Multi-Hierarchy Embedded Platform for Neural Population Simulations.

IEEE transactions on biomedical circuits and systems
Brain-inspired structured neural circuits are the cornerstones of both computational and perceived intelligence. Real-time simulations of large-scale high-dimensional neural populations with complex nonlinearities pose a significant challenge. Taking...

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Nature communications
Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing...

MedGAN: optimized generative adversarial network with graph convolutional networks for novel molecule design.

Scientific reports
Generative Artificial Intelligence can be an important asset in the drug discovery process to meet the demand for novel medicines. This work outlines the optimization and fine-tuning steps of MedGAN, a deep learning model based on Wasserstein Generat...