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Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

Yu Q, Tang H, Tan KC, Li H - PLoS ONE (2013)

Bottom Line: The PSD rule is further validated on a practical example of an optical character recognition problem.The results again show that it can achieve a good recognition performance with a proper encoding.Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.

ABSTRACT
A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

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Related in: MedlinePlus

Illustration of the adaptive learning of the changed target trains.Each dot denotes a spike. At the beginning, the neuron is trained to learn one target (denoted by the light blue bars). After 25 epochs of learning (the dashed red line), the target is changed to another randomly generated train (denoted by the green bars). The right figure shows the distance between the actual output spike train and the target spike train along the learning process.
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pone-0078318-g005: Illustration of the adaptive learning of the changed target trains.Each dot denotes a spike. At the beginning, the neuron is trained to learn one target (denoted by the light blue bars). After 25 epochs of learning (the dashed red line), the target is changed to another randomly generated train (denoted by the green bars). The right figure shows the distance between the actual output spike train and the target spike train along the learning process.

Mentions: At the beginning, the neuron is trained to learn a target train as in the previous tasks. After one successful learning, the target spike train is changed to another arbitrarily generated train, where the precise spike time and the firing rate are different from the previous target. We discover that, with the PSD learning rule, we successfully train the neuron to learn the new target within several epochs. As shown in Fig. 5, during learning, the neuron gradually adapts its firing status from the old target to the new target.


Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

Yu Q, Tang H, Tan KC, Li H - PLoS ONE (2013)

Illustration of the adaptive learning of the changed target trains.Each dot denotes a spike. At the beginning, the neuron is trained to learn one target (denoted by the light blue bars). After 25 epochs of learning (the dashed red line), the target is changed to another randomly generated train (denoted by the green bars). The right figure shows the distance between the actual output spike train and the target spike train along the learning process.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3818323&req=5

pone-0078318-g005: Illustration of the adaptive learning of the changed target trains.Each dot denotes a spike. At the beginning, the neuron is trained to learn one target (denoted by the light blue bars). After 25 epochs of learning (the dashed red line), the target is changed to another randomly generated train (denoted by the green bars). The right figure shows the distance between the actual output spike train and the target spike train along the learning process.
Mentions: At the beginning, the neuron is trained to learn a target train as in the previous tasks. After one successful learning, the target spike train is changed to another arbitrarily generated train, where the precise spike time and the firing rate are different from the previous target. We discover that, with the PSD learning rule, we successfully train the neuron to learn the new target within several epochs. As shown in Fig. 5, during learning, the neuron gradually adapts its firing status from the old target to the new target.

Bottom Line: The PSD rule is further validated on a practical example of an optical character recognition problem.The results again show that it can achieve a good recognition performance with a proper encoding.Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.

ABSTRACT
A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Show MeSH
Related in: MedlinePlus