This system loads and stores a two-dimensional value in a working memory; when the “gate” signal is on (closed), the system maintains its current value through recurrent activity, and when the “gate” is off (open), the system replaces its current representation with the input value. Gray circles are LIF populations while blue circles are detailed neuron populations. The green connection is trained by osNEF to compute f(x) = x, while the purple connection is trained to compute f(x) = −x. The orange connection directly inhibits neurons in “diff” using fixed negative weights.</p
This work is based on a logical neuron model without weights, the Random Access Memory [1]. For the ...
<p>(<b>A</b>) Weight matrix of 117 excitatory neurons in a WTA network. After learning the network e...
<p>Left: map of network-averaged firing rate vs. the strength of functional ...
This network extends the training network in Fig 2, represented by components with the gray backgrou...
The top half of the figure is the “oracle” stream, where the desired filters and transformations are...
<p><b>a</b> A schematic representation of the neural network architecture. Here we show stage 1 and ...
(A) Network structure emerging after learning 2 training stimuli. The modeled neuronal populations a...
<div><p>Excitatory and inhibitory neurons (represented by green and red dots, respectively) are arra...
(A) An LSTM is a type of RNN (greyed neural network; dots represent neurons, arrows represent connec...
Connectivity-reduction C++ code. The program is operated under windows with a gnu complier, and Ran...
<p>(<b>A</b>) Schematic diagram of the neural network. Each red (blue) circle represents an auditory...
<p>There are two populations of neurons, excitatory (green) and inhibitory (red). The inhibitory net...
This network extends Fig 10 by (a) replacing “inh” with a population of detailed inhibitory interneu...
<p>The circuit diagrams show that neurons with excitatory and inhibitory inputs and neurons that hav...
The magnitude and apparent complexity of the brain's connectivity have left explicit networks largel...
This work is based on a logical neuron model without weights, the Random Access Memory [1]. For the ...
<p>(<b>A</b>) Weight matrix of 117 excitatory neurons in a WTA network. After learning the network e...
<p>Left: map of network-averaged firing rate vs. the strength of functional ...
This network extends the training network in Fig 2, represented by components with the gray backgrou...
The top half of the figure is the “oracle” stream, where the desired filters and transformations are...
<p><b>a</b> A schematic representation of the neural network architecture. Here we show stage 1 and ...
(A) Network structure emerging after learning 2 training stimuli. The modeled neuronal populations a...
<div><p>Excitatory and inhibitory neurons (represented by green and red dots, respectively) are arra...
(A) An LSTM is a type of RNN (greyed neural network; dots represent neurons, arrows represent connec...
Connectivity-reduction C++ code. The program is operated under windows with a gnu complier, and Ran...
<p>(<b>A</b>) Schematic diagram of the neural network. Each red (blue) circle represents an auditory...
<p>There are two populations of neurons, excitatory (green) and inhibitory (red). The inhibitory net...
This network extends Fig 10 by (a) replacing “inh” with a population of detailed inhibitory interneu...
<p>The circuit diagrams show that neurons with excitatory and inhibitory inputs and neurons that hav...
The magnitude and apparent complexity of the brain's connectivity have left explicit networks largel...
This work is based on a logical neuron model without weights, the Random Access Memory [1]. For the ...
<p>(<b>A</b>) Weight matrix of 117 excitatory neurons in a WTA network. After learning the network e...
<p>Left: map of network-averaged firing rate vs. the strength of functional ...