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The input neuron in the first layer that detects the image edges are direction-selective.
First, each neuron in the first hidden layer, instead of connecting to all input neurons, is only connected to a small region of them.
In order to reach the best results, the number of neuron in the first hidden layer (tansig) were changed from 1 up to 10, and for the second one, the number remained 1 consistently.
x = ( x 1, x 2, …, x l ) T∈∈ Ω ⊂ R l, Ω is a compact set with smooth boundary ∂ Ω and mes Ω > 0 in the space R l ; u = ( u 1, u 2, …, u m ) T ∈ R m, v = ( v 1, v 2, …, v n ) T ∈ R n. u i ( t, x ) and v j ( t, x ) represent the state of the i th neuron in the first layer and the j th neuron in the second layer at time t and in the space x, respectively.
The bottom panels in Fig. 8 show membrane potentials of the CN neuron in the first, 18th and 19th conditioning trials.
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The optimized AIS model with three input parameters, five neurons in first layer with tangent sigmoid transfer function, and one neuron in the second layer with linear transfer function was obtained.
One smaller neuron in the second thoracic neuromere near each Tv soma was also FMRFa-IR (asterisk in A1); its neurites could however not be visualized.
Consequently each neuron in the second layer received relatively weak inhibition, which was not sufficiently strong to cancel out the excitatory activation of the neurons at the figure location.
The number of neurons in the first layer depends on the degree of wavelet decomposition.
In addition, numbers of neurons in the first and second layers are three and one neurons, respectively.
The ANN used is an Elman network with two hidden layers; having 20 neurons in the first hidden layer, three neurons in the second hidden layer, and four neurons in the output layer.
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