Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Given an input of x1 and x2 for the two input neurons, calculate the
Document related concepts
Multielectrode array wikipedia , lookup
Nonsynaptic plasticity wikipedia , lookup
Caridoid escape reaction wikipedia , lookup
Neuroanatomy wikipedia , lookup
Artificial general intelligence wikipedia , lookup
Holonomic brain theory wikipedia , lookup
Stimulus (physiology) wikipedia , lookup
Single-unit recording wikipedia , lookup
Neural oscillation wikipedia , lookup
Mirror neuron wikipedia , lookup
Premovement neuronal activity wikipedia , lookup
Neural modeling fields wikipedia , lookup
Feature detection (nervous system) wikipedia , lookup
Neural engineering wikipedia , lookup
Pattern recognition wikipedia , lookup
Circumventricular organs wikipedia , lookup
Metastability in the brain wikipedia , lookup
Pre-Bötzinger complex wikipedia , lookup
Neural coding wikipedia , lookup
Optogenetics wikipedia , lookup
Artificial neural network wikipedia , lookup
Neuropsychopharmacology wikipedia , lookup
Catastrophic interference wikipedia , lookup
Development of the nervous system wikipedia , lookup
Central pattern generator wikipedia , lookup
Channelrhodopsin wikipedia , lookup
Biological neuron model wikipedia , lookup
Convolutional neural network wikipedia , lookup
Synaptic gating wikipedia , lookup
Nervous system network models wikipedia , lookup
Transcript
Given an input of x1 and x2 for the two input neurons, calculate the value of the output neuron Y1 in the artificial neural network shown in Figure 1. Use a step function with transition value at 0 to calculate the output from a neuron. Calculate the value of Y1 for values of x1 and x2 equal to (0,0), (1,1), (1,0), and (0,1) and fill out the table below. Can this network describe higher order sequence correlations (like the XOR function)? Figure 1. Artificial neural network with two input neurons, one hidden layer with two neurons, and one output neuron. The artificial neural network uses a step function with transition value at 0 to calculate the output from the neurons. The step function is given by O(o) = 1 if o>0 otherwise 0. X1 0 0 1 1 X2 O 0 1 0 1 Now, we include the “bias” for the input to each neuron. This can be implemented as an addition input neuron to each layer that always has the value 1 (see figure 2). With this additional bias, calculate the value of Y1 for values of x1 and x2 equal to (0,0), (1,1), (1,0), and (0,1) and fill out the table below. Can this network describe higher order sequence correlations (like the XOR function)? Step function as in figure 1 Figure 2. Artificial neural network with two input neurons, one hidden layer with two neurons, and one output neuron. The artificial neural network uses a step function with transition value at 0 to calculate the output from the neurons. The step function is given by O(o) = 1 if o>0 otherwise 0. X1 0 0 1 1 X2 O 0 1 0 1
