Assertion: neural network's neuron | /r/HasProperty | connected to each other
Facets:
value: in a real nervous system
type: OTHER
value: in various patterns
type: OTHER
value: to allow
type: PURPOSE
value: by links
type: MANNER
value: by synapsis
type: OTHER
value: by ability
type: CAUSE
value: by huge flexibility
type: CAUSE
value: by excitatory or inhibitory synapses
type: CAUSE
value: in complex circuits
type: LOCATION
Source sentences:
text: Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others.
text: As in biological neural systems, these neurons are connected with each other and together they have the great processing power.
text: Remember that a neural network is made up of neurons connected to each other; at the same time, each connection of our neural network is associated with a weight that dictates the importance of this relationship in the neuron when multiplied by the input value.
text: Neurons are connected to each other in various patterns, to allow the output of some neurons to become the input of others.
text: It is defined by a bunch of neurons connected to each other.
text: The neurons are connected to each other.
text: When intrinsically active neurons are connected to each other in complex circuits, the possibilities for generating intricate temporal patterns become far more extensive.
text: When neurons that are intrinsically rhythmic are connected to each other by excitatory or inhibitory synapses, the resulting networks are capable of a wide variety of dynamical behaviors, including attractor dynamics, periodicity, and even chaos.
text: This picture shows only 2 neurons connected to each other.
text: In a real nervous system, these neurons are connected to each other by synapsis, which gives this entire system enormous processing power, ability to learn and huge flexibility.
text: The neuron is connected to each other by links and they interact with each other using that link.
text: The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.