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...main roles for starburst amacrine cell have been characterized. SACs are (1) important in the computation of direction-selectivity and they also serve
.... The dendrites here are relatively thin, with a diameter not greater than 1.5 micrometers at their origin. These dendrites taper as we progress radiall
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...and Territories of Bipolar Cells in the Mouse Retina"] J. Neurosci. '29' (1):106-117 doi:[http://dx.doi.org/10.1523%2FJNEUROSCI.4442-08.2009 10.1523/JN
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== Physiology == <!--T:1-->
...en the intensity contrast between the strip of light and the background is 1/5.
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...Robert E. "Retinal Neurotransmitters." ''The Visual Neurosciences''. Vol. 1. Cambridge: The MIT Press, 2003. 304-319.</ref> It should be noted, though,
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...linear neuron with (initially random, uniformly distributed between -1 and 1) synaptic weights from the inputs:
...ere are <em>N</em> input patterns. If we set the learning rate η equal to 1/<em>N</em>, then the update rule becomes
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...roximated by a Taylor series with terms of [[File:Oja2.png]] ignored for n>1 since η is small.
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...the <em>k</em> neurons with the highest outputs have their outputs set to 1, while the rest have their outputs set to 0.
...ability of that input or output (or combination of input and output) being 1. Thus:
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Given a set of k-dimensional inputs with values between 0 and 1 represented as a column vector:
...linear neuron with (initially random, uniformly distributed between -1 and 1) synaptic weights from the inputs:
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...oach to adaptive neural computation"]. <em>Neural Computation</em> <strong>1</strong>: 161-172</ref> It is a ''supervised'' learning technique, meaning
Given a set of k-dimensional inputs with values between 0 and 1 represented as a column vector:
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Given a set of k-dimensional inputs with values between 0 and 1 represented as a column vector:
...linear neuron with (initially random, uniformly distributed between -1 and 1) synaptic weights from the inputs:
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...<em>i</em> indicates the probability that neuron <em>i</em> will output a 1. [[File:RBM3.png]] is the logistic sigmoidal function:
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