<p>(a) Percentage of successful trials as a function of the pattern frequency (pattern duration/the total duration, given a fixed pattern length of 50 ms). The pattern needs to be consistently present, at least at the beginning, for the STDP to start the learning process. (b) Percentage of successful trials as a function of jitter. For jitter greater than 3 ms spike coincidences are lost and the STDP weight updates are inaccurate, so the learning is impaired (c) Percentage of successful trials as a function of the proportion of afferents involved in the pattern. Performance is good if this proportion is above 1/3 (d) Percentage of successful trials as a function of the initial weights. With a high value the neuron can handle more discharges...
<p>The top row presents the case where the noise comes from the input spike jitters. The bottom row ...
<p>The trained neuron receives inputs from 500 neurons. The spike trains received from these neurons...
<p>(<b>A</b>) Top: Trained Tempotron weights <i>w</i><sub><i>i</i></sub>. Bottom: example cross-tria...
<p>Synaptic state matching (+SSM) is crucial for accuracy and long term stability. In the (-SSM) sim...
<p>STDP strength is scale in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0...
<p>Left: The VP distance between the actual and the target output spike train. Center: The timing di...
<p>(<b>A</b>). Responses to single puffs of pheromone. Asterisks indicate significant differences be...
<p>A. End-of-trial errors under different learning conditions: no learning, reward-only, punisher-on...
<p>The averaged weights after learning are shown. The height of each bar reflects the synaptic stren...
<p><b>A:</b> Firing rates of reinforced (blue), surround (red), and control (green) neurons after le...
<p>The neuron is trained in a maximum number of 500 epochs to correctly memorize a set of 10 spike p...
<p>The results are averaged over 10 neurons and 100 s with the same configuration as in <a href="htt...
<p>The robustness of the iSTDP learning rule for stochastic spiking inputs was checked by comparing ...
(A) Behavior of output neurons (MBONs) during first-order conditioning. During training, a CS+ (blue...
<p>(<i>A</i>) Synaptic weight matrices for different latency conduction delays (1–5 time steps). Pos...
<p>The top row presents the case where the noise comes from the input spike jitters. The bottom row ...
<p>The trained neuron receives inputs from 500 neurons. The spike trains received from these neurons...
<p>(<b>A</b>) Top: Trained Tempotron weights <i>w</i><sub><i>i</i></sub>. Bottom: example cross-tria...
<p>Synaptic state matching (+SSM) is crucial for accuracy and long term stability. In the (-SSM) sim...
<p>STDP strength is scale in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0...
<p>Left: The VP distance between the actual and the target output spike train. Center: The timing di...
<p>(<b>A</b>). Responses to single puffs of pheromone. Asterisks indicate significant differences be...
<p>A. End-of-trial errors under different learning conditions: no learning, reward-only, punisher-on...
<p>The averaged weights after learning are shown. The height of each bar reflects the synaptic stren...
<p><b>A:</b> Firing rates of reinforced (blue), surround (red), and control (green) neurons after le...
<p>The neuron is trained in a maximum number of 500 epochs to correctly memorize a set of 10 spike p...
<p>The results are averaged over 10 neurons and 100 s with the same configuration as in <a href="htt...
<p>The robustness of the iSTDP learning rule for stochastic spiking inputs was checked by comparing ...
(A) Behavior of output neurons (MBONs) during first-order conditioning. During training, a CS+ (blue...
<p>(<i>A</i>) Synaptic weight matrices for different latency conduction delays (1–5 time steps). Pos...
<p>The top row presents the case where the noise comes from the input spike jitters. The bottom row ...
<p>The trained neuron receives inputs from 500 neurons. The spike trains received from these neurons...
<p>(<b>A</b>) Top: Trained Tempotron weights <i>w</i><sub><i>i</i></sub>. Bottom: example cross-tria...