AI Medical Compendium Topic

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Neurons

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A deep learning strategy to identify cell types across species from high-density extracellular recordings.

Cell
High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals an...

Exploring temporal information dynamics in Spiking Neural Networks: Fast Temporal Efficient Training.

Journal of neuroscience methods
BACKGROUND: Spiking Neural Networks (SNNs) hold significant potential in brain simulation and temporal data processing. While recent research has focused on developing neuron models and leveraging temporal dynamics to enhance performance, there is a ...

Robotic Fast Patch Clamp in Brain Slices Based on Stepwise Micropipette Navigation and Gigaseal Formation Control.

Sensors (Basel, Switzerland)
The patch clamp technique has become the gold standard for neuron electrophysiology research in brain science. Brain slices have been widely utilized as the targets of the patch clamp technique due to their higher optical transparency compared to a l...

An accurate and fast learning approach in the biologically spiking neural network.

Scientific reports
Computations adapted from the interactions of neurons in the nervous system have the potential to be a strong foundation for building computers with cognitive functions including decision-making, generalization, and real-time learning. In this contex...

Organic Artificial Nerves: Neuromorphic Robotics and Bioelectronics.

Chemical reviews
Neuromorphic electronics are inspired by the human brain's compact, energy-efficient nature and its parallel-processing capabilities. Beyond the brain, the entire human nervous system, with its hierarchical structure, efficiently preprocesses complex...

Spiking neural networks on FPGA: A survey of methodologies and recent advancements.

Neural networks : the official journal of the International Neural Network Society
The mimicry of the biological brain's structure in information processing enables spiking neural networks (SNNs) to exhibit significantly reduced power consumption compared to conventional systems. Consequently, these networks have garnered heightene...

MARBLE: interpretable representations of neural population dynamics using geometric deep learning.

Nature methods
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representatio...

Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.

Scientific reports
Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a challenge in many areas of science. In neuroscience, techniques for estimating connectivity are paramou...

Learning in Wilson-Cowan Model for Metapopulation.

Neural computation
The Wilson-Cowan model for metapopulation, a neural mass network model, treats different subcortical regions of the brain as connected nodes, with connections representing various types of structural, functional, or effective neuronal connectivity be...

Active Inference and Intentional Behavior.

Neural computation
Recent advances in theoretical biology suggest that key definitions of basal cognition and sentient behavior may arise as emergent properties of in vitro cell cultures and neuronal networks. Such neuronal networks reorganize activity to demonstrate s...