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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

Performance on OCR task.The left shows the association ability of the neuron to map a typical digit with the desired spike train. Digit “8” is used as an example here. The distance between the output spike train and the desired spike train is depicted versus the noise level. The right shows the classification accuracy on the testing set. Solid lines denote the average and shaded areas denote the standard deviation. All the data are averaged over 30 runs.
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pone-0078318-g015: Performance on OCR task.The left shows the association ability of the neuron to map a typical digit with the desired spike train. Digit “8” is used as an example here. The distance between the output spike train and the desired spike train is depicted versus the noise level. The right shows the classification accuracy on the testing set. Solid lines denote the average and shaded areas denote the standard deviation. All the data are averaged over 30 runs.

Mentions: Fig. 15 shows the testing results. In order to observe the association ability of the neuron to map a digit with the desired spike train, digit “8” is used as an example. The neuron corresponding to digit “8” can successfully produce a spike train close to the target train when the noise level is low. This association worsens as the noise level increases. As shown in Fig. 15, the classification accuracy remains high when the noise level is low and will drop gradually with increasing noise level. Even when the image is seriously damaged by the noise ( noise level), a high accuracy of around can still be obtained. The results show that the trained neurons can successfully associate the template images with the target spike train. Moreover, the trained neurons present a high recognition ability under the relative confidence criterion even if images are damaged by noise.


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

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

Performance on OCR task.The left shows the association ability of the neuron to map a typical digit with the desired spike train. Digit “8” is used as an example here. The distance between the output spike train and the desired spike train is depicted versus the noise level. The right shows the classification accuracy on the testing set. Solid lines denote the average and shaded areas denote the standard deviation. All the data are averaged over 30 runs.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0078318-g015: Performance on OCR task.The left shows the association ability of the neuron to map a typical digit with the desired spike train. Digit “8” is used as an example here. The distance between the output spike train and the desired spike train is depicted versus the noise level. The right shows the classification accuracy on the testing set. Solid lines denote the average and shaded areas denote the standard deviation. All the data are averaged over 30 runs.
Mentions: Fig. 15 shows the testing results. In order to observe the association ability of the neuron to map a digit with the desired spike train, digit “8” is used as an example. The neuron corresponding to digit “8” can successfully produce a spike train close to the target train when the noise level is low. This association worsens as the noise level increases. As shown in Fig. 15, the classification accuracy remains high when the noise level is low and will drop gradually with increasing noise level. Even when the image is seriously damaged by the noise ( noise level), a high accuracy of around can still be obtained. The results show that the trained neurons can successfully associate the template images with the target spike train. Moreover, the trained neurons present a high recognition ability under the relative confidence criterion even if images are damaged by noise.

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