<p>The vertical axis corresponds to the network’s 300 model neurons, and the horizontal axis corresponds to the input sequence element of the total length 20. (A) Joint probability of inputs and neuron firing in SORN with plasticity in the first block of training. (B) Joint probability of inputs and neuron firing in SORN with plasticity in the last block of training. Compared with the left subplot, the joint probability becomes higher with training and the firing of neurons is sparse. (C) Joint probability of inputs and neuron firing in SORN without STDP and IP.</p
<p>The entry in layers <i>i</i> (row) and <i>j</i> (column) represents the probability that a neuron...
In connection with model of the neuron presented by CAIANIELLO (1961), an approach to the study of a...
<p>The entry in layers <i>i</i> (row) and <i>j</i> (column) represents the probability that a neuron...
(a) The demonstration network consists of populations of excitatory (red dots), inhibitory (blue dot...
<p>(A) Network architecture with 21×6 inputs and 7×3 network neurons. The green, red and blue neuron...
<p>Each neuron in population receives randomly drawn excitatory inputs with weight , randomly dra...
<p><b>A,B.</b> Top: The – curves (green) for GS neurons ( pS/µm<sup>2</sup> and pS/µm<sup>2</sup>) ...
<p>Two representative neurons embedded in a network are shown with their reciprocal synapses (middle...
<p>The figures on the left hand side column show the typical probabilities of a synapse from a neuro...
<p>(<b>A</b>) Probability per unit time (spike rate) of a single neuron. Top, in red, experimental d...
<p>(a) The probability that a single neuron in the ground state (receiving homogenous background in...
<p>The entry in layers <i>i</i> (row) and <i>j</i> (column) represents the probability that a neuron...
<p>The training data set “MG” is used. Neuron 1 (output neuron): (A) Initial input distribution. (B)...
<p><b>a.</b> Example of the network's ability to reconstruct its inputs after training using depress...
<p>The trained neuron receives inputs from 500 neurons. The spike trains received from these neurons...
<p>The entry in layers <i>i</i> (row) and <i>j</i> (column) represents the probability that a neuron...
In connection with model of the neuron presented by CAIANIELLO (1961), an approach to the study of a...
<p>The entry in layers <i>i</i> (row) and <i>j</i> (column) represents the probability that a neuron...
(a) The demonstration network consists of populations of excitatory (red dots), inhibitory (blue dot...
<p>(A) Network architecture with 21×6 inputs and 7×3 network neurons. The green, red and blue neuron...
<p>Each neuron in population receives randomly drawn excitatory inputs with weight , randomly dra...
<p><b>A,B.</b> Top: The – curves (green) for GS neurons ( pS/µm<sup>2</sup> and pS/µm<sup>2</sup>) ...
<p>Two representative neurons embedded in a network are shown with their reciprocal synapses (middle...
<p>The figures on the left hand side column show the typical probabilities of a synapse from a neuro...
<p>(<b>A</b>) Probability per unit time (spike rate) of a single neuron. Top, in red, experimental d...
<p>(a) The probability that a single neuron in the ground state (receiving homogenous background in...
<p>The entry in layers <i>i</i> (row) and <i>j</i> (column) represents the probability that a neuron...
<p>The training data set “MG” is used. Neuron 1 (output neuron): (A) Initial input distribution. (B)...
<p><b>a.</b> Example of the network's ability to reconstruct its inputs after training using depress...
<p>The trained neuron receives inputs from 500 neurons. The spike trains received from these neurons...
<p>The entry in layers <i>i</i> (row) and <i>j</i> (column) represents the probability that a neuron...
In connection with model of the neuron presented by CAIANIELLO (1961), an approach to the study of a...
<p>The entry in layers <i>i</i> (row) and <i>j</i> (column) represents the probability that a neuron...