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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.

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The kernels used in the simulation of the integrate-and-fire neuron.(A) The  kernel. (B), (C) The  kernel. In (B) there is no postsynaptic spike. In (C), a postsynaptic spike is fired at  ms. A presynaptic spike is received at .
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pone-0040233-g019: The kernels used in the simulation of the integrate-and-fire neuron.(A) The kernel. (B), (C) The kernel. In (B) there is no postsynaptic spike. In (C), a postsynaptic spike is fired at ms. A presynaptic spike is received at .

Mentions: In order to be able to test the learning rules in a computer simulation, we must define the forms of the and kernels of the Spike Response Model. We define them to correspond to the classical leaky integrate-and-fire neural model, which is a particular case of the Spike Response Model [21]. A further choice must be made for the form of the synaptic currents. We modeled the kernel that reflects the form of the synaptic current generated by the arrival of a presynaptic spike through the synapse at the timing as a difference of two exponentials (double-exponential current):(30)for , where and are positive parameters (time constants). The kernel is illustrated in Fig. 19 A.


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

Florian RV - PLoS ONE (2012)

The kernels used in the simulation of the integrate-and-fire neuron.(A) The  kernel. (B), (C) The  kernel. In (B) there is no postsynaptic spike. In (C), a postsynaptic spike is fired at  ms. A presynaptic spike is received at .
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3412872&req=5

pone-0040233-g019: The kernels used in the simulation of the integrate-and-fire neuron.(A) The kernel. (B), (C) The kernel. In (B) there is no postsynaptic spike. In (C), a postsynaptic spike is fired at ms. A presynaptic spike is received at .
Mentions: In order to be able to test the learning rules in a computer simulation, we must define the forms of the and kernels of the Spike Response Model. We define them to correspond to the classical leaky integrate-and-fire neural model, which is a particular case of the Spike Response Model [21]. A further choice must be made for the form of the synaptic currents. We modeled the kernel that reflects the form of the synaptic current generated by the arrival of a presynaptic spike through the synapse at the timing as a difference of two exponentials (double-exponential current):(30)for , where and are positive parameters (time constants). The kernel is illustrated in Fig. 19 A.

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.

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