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Each answer generated slightly different types of neural activity.
They can turn into different types of neural cells but probably not into others, like kidney or liver cells.
Many types of neural stem cell secrete a mix of molecules that unlock suppressed growth pathways in nerves.
Both groups of researchers employed similar approaches, weaving together two types of neural networks, one focused on recognizing images and the other on human language.
The training of some types of neural networks leads to separable non-linear least squares problems.
Two types of neural network architectures, feedforward and Elman recurrent, are used in this study.
The investigators generated both types of neural spheroids from their Timothy-syndrome tissue samples, fused them and watched to see what would happen.
Recent advanced studies have demonstrated that cytokines and extracellular matrix (ECM) could trigger various types of neural differentiation.
There exist many types of neural networks that solve a wide range of problems in the area of image processing.
This chapter discusses three types of neural networks: multilayer perceptrons, finite impulse response (FIR) multilayer perceptrons, and recurrent neural networks.
We show that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics.
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