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In this section, the proposed method is evaluated by simulating a neural network model using combined data from more than 10 management areas (MAs).
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In this work, we address these questions by simulating a two time-scale biologically realistic neural network with dynamics evolving at two time-scales: the fast scale of neurons and synapses and slow scale of homeostatic presynaptic-dependent synaptic scaling.
They're already involved in the complex math of astrophysics, and solved the problem using C++ libraries that simulated a neural network.
We then simulate a local neural network to assess the possible relevance of attractor states and spike-rate adaptation to the observed neural dynamics.
At every iteration, the neural net works by simulating both an approach to the degradation process (backward) and to the restoration solution (forward), while refining the results according to a optimization criteria.
Much recent effort has been directed towards understanding the mechanisms that underlie neural system development and plasticity by simulating biological processes at a time- scale of individual development.
Deep learning aims at building a deep neural network model by simulating the learning process of brain.
On the other hand, it is possible to study algorithms for neural computation by simulating, or mathematically analyzing, the operations of simplified "units" that have some of the properties of neurons but abstract out much of their biological complexity.
We simulated a perturbation of neural dynamics in a biophysically informed model of decision-making through in silico depolarization at the level of neuronal ensembles.
We simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next.
When artificial intelligence coalesced into a field of study from the fog of information science after World War II, scientists first thought that they could simulate a brain by building neural networks assembled from vast arrays of switches, which would mimic synapses.
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