Limits...
The chronotron: a neuron that learns to fire temporally precise spike patterns.

Florian RV - PLoS ONE (2012)

Bottom Line: When the input is noisy, the classification also leads to noise reduction.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.

View Article: PubMed Central - PubMed

Affiliation: Center for Cognitive and Neural Studies, Romanian Institute of Science and Technology, Cluj-Napoca, Romania. florian@rist.ro

ABSTRACT
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.

Show MeSH

Related in: MedlinePlus

The dependence of chronotron performance on when synapses are updated during simulations.The number of learning epochs required for correct learning as a function of the load , for various methods of applying the synaptic changes according to the learning rules: batch updating (synapses are changed at the end of each batch of  trials, each one corresponding to one of the input patterns); trial updating (synapses are changed at the end of each trial); online updating (synapses are changed after each target or actual postsynaptic spike — for I-learning only). (A) E-learning. (B) I-learning.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3412872&req=5

pone-0040233-g018: The dependence of chronotron performance on when synapses are updated during simulations.The number of learning epochs required for correct learning as a function of the load , for various methods of applying the synaptic changes according to the learning rules: batch updating (synapses are changed at the end of each batch of trials, each one corresponding to one of the input patterns); trial updating (synapses are changed at the end of each trial); online updating (synapses are changed after each target or actual postsynaptic spike — for I-learning only). (A) E-learning. (B) I-learning.

Mentions: In our simulations, the synaptic changes defined by the learning rules were accumulated and were applied to the synapses at the end of each batch consisting of trials (presentations of the input patterns) [41], [42]. Simulations of E-learning where synaptic changes were applied at the end of each trial required a slightly higher number of epochs for correct learning, but led to the same memory capacity (Fig. 18 A). Simulations of I-learning where synaptic changes were applied either at the end of each trial or online, triggered by postsynaptic spikes (as in Fig. 5) did not lead to results significantly different than simulations with batch updating of the synapses (Fig. 18 B).


The chronotron: a neuron that learns to fire temporally precise spike patterns.

Florian RV - PLoS ONE (2012)

The dependence of chronotron performance on when synapses are updated during simulations.The number of learning epochs required for correct learning as a function of the load , for various methods of applying the synaptic changes according to the learning rules: batch updating (synapses are changed at the end of each batch of  trials, each one corresponding to one of the input patterns); trial updating (synapses are changed at the end of each trial); online updating (synapses are changed after each target or actual postsynaptic spike — for I-learning only). (A) E-learning. (B) I-learning.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3412872&req=5

pone-0040233-g018: The dependence of chronotron performance on when synapses are updated during simulations.The number of learning epochs required for correct learning as a function of the load , for various methods of applying the synaptic changes according to the learning rules: batch updating (synapses are changed at the end of each batch of trials, each one corresponding to one of the input patterns); trial updating (synapses are changed at the end of each trial); online updating (synapses are changed after each target or actual postsynaptic spike — for I-learning only). (A) E-learning. (B) I-learning.
Mentions: In our simulations, the synaptic changes defined by the learning rules were accumulated and were applied to the synapses at the end of each batch consisting of trials (presentations of the input patterns) [41], [42]. Simulations of E-learning where synaptic changes were applied at the end of each trial required a slightly higher number of epochs for correct learning, but led to the same memory capacity (Fig. 18 A). Simulations of I-learning where synaptic changes were applied either at the end of each trial or online, triggered by postsynaptic spikes (as in Fig. 5) did not lead to results significantly different than simulations with batch updating of the synapses (Fig. 18 B).

Bottom Line: When the input is noisy, the classification also leads to noise reduction.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.

View Article: PubMed Central - PubMed

Affiliation: Center for Cognitive and Neural Studies, Romanian Institute of Science and Technology, Cluj-Napoca, Romania. florian@rist.ro

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus