The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns
In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely te...
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| Format: | Online |
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Public Library of Science
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3412872/ |
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pubmed-34128722012-08-09 The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns Florian, Răzvan V. Research Article In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm. Public Library of Science 2012-08-06 /pmc/articles/PMC3412872/ /pubmed/22879876 http://dx.doi.org/10.1371/journal.pone.0040233 Text en © 2012 Florian http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Florian, Răzvan V. |
| spellingShingle |
Florian, Răzvan V. The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns |
| author_facet |
Florian, Răzvan V. |
| author_sort |
Florian, Răzvan V. |
| title |
The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns |
| title_short |
The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns |
| title_full |
The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns |
| title_fullStr |
The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns |
| title_full_unstemmed |
The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns |
| title_sort |
chronotron: a neuron that learns to fire temporally precise spike patterns |
| description |
In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons), one that provides high memory capacity (E-learning), and one that has a higher biological plausibility (I-learning). With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm. |
| publisher |
Public Library of Science |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3412872/ |
| _version_ |
1611547918102167552 |