<p><b>a–b</b>: Evolution of weights in the spiking model. Weights as learned after different numbers of stimulus presentations are shown for 10 example hidden units. <b>c–d</b>: Attempted reconstructions at different points in training for the two spiking model experiments, for the stimuli shown in the bottom rows. Early in training, the same few hidden units whose incoming weights happened to be strongest were often activated regardless of the stimulus, leading to similar reconstruction attempts for different stimuli (first rows). Over time, the attempted reconstructions came to resemble the input stimuli.</p
<p>Results of a longer simulation (3000 s) including synaptic weight distributions and trajectories....
<p>Left: The VP distance between the actual and the target output spike train. Center: The timing di...
<p>The weights of synaptic strengths on the model before (a) and after (b) training trials. (c) The...
<p><b>a–d:</b> As <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004566...
The Vm traces of spiking (A) and non-spiking (B) simulations with 20 (cyan) to 3 (black) input stimu...
<p>(A) Evolution of synaptic weights in the neural circuit on inputs from the MNIST database. (B) Ev...
<p><b>(A)</b> Evolution of synaptic weights in the network during plasticity. After each batch of le...
Networks from three different replicates from simulations with LE = 20 are shown (three of the four ...
<p>The LIF and IM neuron models are considered. The left panel shows the connection setup of the exp...
<p>The top and the middle show the averaged weights before and after learning, respectively. The hei...
<p><b>A, B</b>: Visualization of hidden structure in the spike inputs shown in D, E: Each row in pa...
<p>Architecture of the model network and network activity. <b>a:</b> Architecture of the model netwo...
<p>The averaged weights after learning are shown. The height of each bar reflects the synaptic stren...
<p>(A) An example set of generative fields , for ( pixels). Due to the normalization, different rec...
<p>The trained neuron receives inputs from 500 neurons. The spike trains received from these neurons...
<p>Results of a longer simulation (3000 s) including synaptic weight distributions and trajectories....
<p>Left: The VP distance between the actual and the target output spike train. Center: The timing di...
<p>The weights of synaptic strengths on the model before (a) and after (b) training trials. (c) The...
<p><b>a–d:</b> As <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004566...
The Vm traces of spiking (A) and non-spiking (B) simulations with 20 (cyan) to 3 (black) input stimu...
<p>(A) Evolution of synaptic weights in the neural circuit on inputs from the MNIST database. (B) Ev...
<p><b>(A)</b> Evolution of synaptic weights in the network during plasticity. After each batch of le...
Networks from three different replicates from simulations with LE = 20 are shown (three of the four ...
<p>The LIF and IM neuron models are considered. The left panel shows the connection setup of the exp...
<p>The top and the middle show the averaged weights before and after learning, respectively. The hei...
<p><b>A, B</b>: Visualization of hidden structure in the spike inputs shown in D, E: Each row in pa...
<p>Architecture of the model network and network activity. <b>a:</b> Architecture of the model netwo...
<p>The averaged weights after learning are shown. The height of each bar reflects the synaptic stren...
<p>(A) An example set of generative fields , for ( pixels). Due to the normalization, different rec...
<p>The trained neuron receives inputs from 500 neurons. The spike trains received from these neurons...
<p>Results of a longer simulation (3000 s) including synaptic weight distributions and trajectories....
<p>Left: The VP distance between the actual and the target output spike train. Center: The timing di...
<p>The weights of synaptic strengths on the model before (a) and after (b) training trials. (c) The...