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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 temporal sequence learning of a typical run.The neuron is connected with  synapses, and is trained to reproduce spikes at the target time (denoted as light blue bars in the middle). The bottom and top show the dynamics of the neuron's potential before and after learning, respectively. The dashed red lines denote the firing threshold. In the middle, each spike is denoted as a dot. The right figure shows the distance between the actual output spike train and the target spike train.
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pone-0078318-g003: Illustration of the temporal sequence learning of a typical run.The neuron is connected with synapses, and is trained to reproduce spikes at the target time (denoted as light blue bars in the middle). The bottom and top show the dynamics of the neuron's potential before and after learning, respectively. The dashed red lines denote the firing threshold. In the middle, each spike is denoted as a dot. The right figure shows the distance between the actual output spike train and the target spike train.

Mentions: Fig. 3 illustrates a typical run of the learning. Initially, the neuron is observed to fire at any arbitrary time and with a firing rate different from the target train, resulting in a large distance value. The actual output spike train is quite different from the target train at the beginning. During the learning process, the neuron gradually learns to produce spikes at the target time, and that is also reflected by the decreasing distance. After finishing the first 10 epochs of learning, both the firing rate and the firing time of the output spikes match those in the target spike train. The dynamics of neuron's membrane potential is also shown in Fig. 3. Whenever the membrane potential exceeds the threshold, a spike is emitted and the potential is kept at reset level for a refractory period. The detailed mathematical description governing this behaviour was presented previously in the section on the Spiking Neuron Model.


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 temporal sequence learning of a typical run.The neuron is connected with  synapses, and is trained to reproduce spikes at the target time (denoted as light blue bars in the middle). The bottom and top show the dynamics of the neuron's potential before and after learning, respectively. The dashed red lines denote the firing threshold. In the middle, each spike is denoted as a dot. The right figure shows the distance between the actual output spike train and the target spike train.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0078318-g003: Illustration of the temporal sequence learning of a typical run.The neuron is connected with synapses, and is trained to reproduce spikes at the target time (denoted as light blue bars in the middle). The bottom and top show the dynamics of the neuron's potential before and after learning, respectively. The dashed red lines denote the firing threshold. In the middle, each spike is denoted as a dot. The right figure shows the distance between the actual output spike train and the target spike train.
Mentions: Fig. 3 illustrates a typical run of the learning. Initially, the neuron is observed to fire at any arbitrary time and with a firing rate different from the target train, resulting in a large distance value. The actual output spike train is quite different from the target train at the beginning. During the learning process, the neuron gradually learns to produce spikes at the target time, and that is also reflected by the decreasing distance. After finishing the first 10 epochs of learning, both the firing rate and the firing time of the output spikes match those in the target spike train. The dynamics of neuron's membrane potential is also shown in Fig. 3. Whenever the membrane potential exceeds the threshold, a spike is emitted and the potential is kept at reset level for a refractory period. The detailed mathematical description governing this behaviour was presented previously in the section on the Spiking Neuron Model.

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