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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 encoding schema.The left shows the structure of an encoding unit. The encoding unit includes a positive neuron (), a negative neuron () and an output neuron (). Each encoding unit is assigned to a subthreshold membrane oscillation. Both  and  neurons receive signals from this subthreshold membrane oscillation and the corresponding pixel. The  neuron only reacts to positive activation voltage, while the  neuron only reacts to negative activation voltage. The firing of either the  neuron or the  neuron will immediately cause the firing of the  neuron. The right illustrate the dynamics of the encoding. The B/W pixel will cause a downward/upward shift from the subthreshold membrane oscillation. A spike is generated if the membrane potential crosses the threshold line ( and ).
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pone-0078318-g014: Illustration of the encoding schema.The left shows the structure of an encoding unit. The encoding unit includes a positive neuron (), a negative neuron () and an output neuron (). Each encoding unit is assigned to a subthreshold membrane oscillation. Both and neurons receive signals from this subthreshold membrane oscillation and the corresponding pixel. The neuron only reacts to positive activation voltage, while the neuron only reacts to negative activation voltage. The firing of either the neuron or the neuron will immediately cause the firing of the neuron. The right illustrate the dynamics of the encoding. The B/W pixel will cause a downward/upward shift from the subthreshold membrane oscillation. A spike is generated if the membrane potential crosses the threshold line ( and ).

Mentions: An increasing body of evidence shows that action potentials are related to the phases of the intrinsic subthreshold membrane potential oscillations [45]–[47]. These observations support the hypothesis of a phase code. Following the phase code presented in [27], [43], we develop a simple encoding method for this task. The mechanism of our encoding model is illustrated in Fig. 14. The encoding unit consists of a positive neuron (), a negative neuron () and an output neuron (). Each encoding unit is connected to a pixel and a subthreshold membrane potential oscillation. For simplicity, the oscillation for the - encoding unit is described as:(13)where is the magnitude of the subthreshold membrane oscillation, is the phase angular velocity and is the initial phase. is defined as:(14)where is the reference phase and is the phase difference between nearby encoding units. We set where is the number of encoding units. is equal to the number of pixels in the image (400 here). The oscillation period is set to be which corresponds to a frequency of .


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 encoding schema.The left shows the structure of an encoding unit. The encoding unit includes a positive neuron (), a negative neuron () and an output neuron (). Each encoding unit is assigned to a subthreshold membrane oscillation. Both  and  neurons receive signals from this subthreshold membrane oscillation and the corresponding pixel. The  neuron only reacts to positive activation voltage, while the  neuron only reacts to negative activation voltage. The firing of either the  neuron or the  neuron will immediately cause the firing of the  neuron. The right illustrate the dynamics of the encoding. The B/W pixel will cause a downward/upward shift from the subthreshold membrane oscillation. A spike is generated if the membrane potential crosses the threshold line ( and ).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0078318-g014: Illustration of the encoding schema.The left shows the structure of an encoding unit. The encoding unit includes a positive neuron (), a negative neuron () and an output neuron (). Each encoding unit is assigned to a subthreshold membrane oscillation. Both and neurons receive signals from this subthreshold membrane oscillation and the corresponding pixel. The neuron only reacts to positive activation voltage, while the neuron only reacts to negative activation voltage. The firing of either the neuron or the neuron will immediately cause the firing of the neuron. The right illustrate the dynamics of the encoding. The B/W pixel will cause a downward/upward shift from the subthreshold membrane oscillation. A spike is generated if the membrane potential crosses the threshold line ( and ).
Mentions: An increasing body of evidence shows that action potentials are related to the phases of the intrinsic subthreshold membrane potential oscillations [45]–[47]. These observations support the hypothesis of a phase code. Following the phase code presented in [27], [43], we develop a simple encoding method for this task. The mechanism of our encoding model is illustrated in Fig. 14. The encoding unit consists of a positive neuron (), a negative neuron () and an output neuron (). Each encoding unit is connected to a pixel and a subthreshold membrane potential oscillation. For simplicity, the oscillation for the - encoding unit is described as:(13)where is the magnitude of the subthreshold membrane oscillation, is the phase angular velocity and is the initial phase. is defined as:(14)where is the reference phase and is the phase difference between nearby encoding units. We set where is the number of encoding units. is equal to the number of pixels in the image (400 here). The oscillation period is set to be which corresponds to a frequency of .

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