Assertion: neural network's neuron | /r/HasProperty | connected to every neuron
Facets:
value: in the next layer
type: LOCATION
value: through a connection link
type: MANNER
value: via synapses
type: MANNER
value: fully
type: DEGREE
value: through long tendrils
type: MANNER
value: in a simple neural network
type: OTHER
value: in subsequent layers
type: TEMPORAL
value: with certain coefficients
type: OTHER
Source sentences:
text: The term “Fully Connected” implies that every neuron in the previous layer is connected to every neuron on the next layer.
text: A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive.
text: Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer.
text: Every neuron is connected with other neuron through a connection link.
text: A Hopfield network (HN) is a network where every neuron is connected to every other neuron.
text: Please observe that every neuron in each layer is connected to all the neurons in the next layer.
text: Each neuron is connected with another neuron via synapses.
text: Each neuron is connected to other neurons with certain coefficients.
text: The figure depicts a neuron connected with n other neurons and thus receives n inputs (x1, x2, ….. xn).
text: All the neurons in a hidden layer are connected to each and every neuron in the next layer, hence we have a fully connected hidden layers.
text: The last layers in the network are fully connected, meaning that neurons of preceding layers are connected to every neuron in subsequent layers.
text: Instead of neurons being connected to every neuron in the previous layer, they are instead only connected to neurons close to it and all have the same weight.
text: In a simple neural network, like a classic feed-forward neural network, every neuron on a given layer is connected to every neuron on the subsequent layer.
text: Since different parts of a neural network often work on different subtasks, it can be unnecessary for every neuron to be connected to every neuron in the next layer.
text: That is, every neuron in the network is connected to every neuron in adjacent layers:
text: Every neuron is connected to many other neurons through long tendrils called dendrites.
text: Information then flows through layers of neurons, where each neuron is connected to many other neurons.
text: A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive.
text: Each hidden layer is made up of a set of neurons, where each neuron is fully connected to all neurons in the previous layer, and where neurons in a single layer function completely independently and do not share any connections.
text: Each of these layers contains neurons that are connected to neurons in the previous layer, and each neuron has its own weight.
text: Every neuron is connected with other neuron through a connection link.
text: As we have discussed above that every neuron in ANN is connected with other neuron through a connection link and that link is associated with a weight having the information about the input signal.
text: A Hopfield network (HN) is a network where every neuron is connected to every other neuron; it is a completely entangled plate of spaghetti as even all the nodes function as everything.
text: Feed-forward means that the signals are fed in a forward direction through the network; and fully connected means that every neuron in one layer is connected to all the neurons in the next layer.
text: Each neuron is connected with another neuron via synapses.