Presynaptic Spike-Driven Spike Timing-Dependent Plasticity With Address Event Representation for Large-Scale Neuromorphic Systems

Abstract Based on the well-known Fabry–Pérot approach, after taking into account the variation of bias current of the vertical-cavity semiconductor optical amplifier (VCSOA) according to the present synapse weight, we implement the optical spike timing dependent plasticity (STDP) with weight-dependent learning window in a VCSOA with double optical spike injections, and numerically investigate the corresponding weight-dependent STDP characteristics. The simulation results show that, the bias current of VCSOA has significant effect on the optical STDP curve. After introducing an adaptive variation of the bias current according to the present synapse weight, the optical weight-dependent STDP based on VCSOA can be realized. Moreover, the weight training based on the optical weight-dependent STDP can be effectively controlled by adjusting some typical external or intrinsic parameters and the excessive adjusting of synaptic weight is avoided, which can be used to balance the stability and competition among synapses and pave a way for the future large-scale energy efficient optical spiking neural networks based on the weight-dependent STDP learning mechanism.

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Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning

Neural Computation ◽

10.1162/neco.2006.18.6.1318 ◽

2006 ◽

Vol 18 (6) ◽

pp. 1318-1348 ◽

Cited By ~ 130

Author(s):

Jean-Pascal Pfister ◽

Taro Toyoizumi ◽

David Barber ◽

Wulfram Gerstner

Keyword(s):

Supervised Learning ◽

Time Course ◽

Learning Rule ◽

Spike Timing ◽

Excitatory Postsynaptic Potential ◽

Spike Timing Dependent Plasticity ◽

Action Potential Firing ◽

Dependent Plasticity ◽

Potential Firing ◽

Presynaptic Spike

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before the desired postsynaptic spike timing, our optimal learning rule predicts that the synapse should become potentiated. The dependence of the potentiation on spike timing directly reflects the time course of an excitatory postsynaptic potential. However, our approach gives no unique reason for synaptic depression under reversed spike timing. In fact, the presence and amplitude of depression of synaptic efficacies for reversed spike timing depend on how constraints are implemented in the optimization problem. Two different constraints, control of postsynaptic rates and control of temporal locality, are studied. The relation of our results to spike-timing-dependent plasticity and reinforcement learning is discussed.

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Spike-Timing-Dependent-Plasticity in Hybrid Memristive-CMOS Spiking Neuromorphic Systems

Memristors and Memristive Systems ◽

10.1007/978-1-4614-9068-5_12 ◽

2013 ◽

pp. 353-377 ◽

Cited By ~ 1

Author(s):

Teresa Serrano-Gotarredona ◽

Bernabé Linares-Barranco

Keyword(s):

Spike Timing ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity ◽

Neuromorphic Systems

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A Model of Spike-Timing Dependent Plasticity: One or Two Coincidence Detectors?

Journal of Neurophysiology ◽

10.1152/jn.2002.88.1.507 ◽

2002 ◽

Vol 88 (1) ◽

pp. 507-513 ◽

Cited By ~ 95

Author(s):

Uma R. Karmarkar ◽

Dean V. Buonomano

Keyword(s):

Standard Model ◽

Interspike Interval ◽

Spike Timing ◽

Spike Timing Dependent Plasticity ◽

Coincidence Detector ◽

Dependent Plasticity ◽

The Standard Model ◽

Nmda Channels ◽

Presynaptic Spike ◽

Detector Model

In spike-timing dependent plasticity (STDP), synapses exhibit LTD or LTP depending on the order of activity in the presynaptic and postsynaptic cells. LTP occurs when a single presynaptic spike precedes a postsynaptic one (a positive interspike interval, or ISI), while the reverse order of activity (a negative ISI) produces LTD. A fundamental question is whether the “standard model” of plasticity in which moderate increases in Ca2+ influx through the N-methyl-d-aspartate (NMDA) channels induce LTD and large increases induce LTP, can account for the order and interval sensitivity of STDP. To examine this issue we developed a model that captures postsynaptic Ca2+ influx dynamics and the associativity of the NMDA receptors. While this model can generate both LTD and LTP, it predicts that LTD will be observed at both negative and positive ISIs. This is because longer and longer positive ISIs induce monotonically decreasing levels of Ca2+, which eventually fall into the same range that produced LTD at negative ISIs. A second model that incorporated a second coincidence detector in addition to the NMDA receptor generated LTP at positive intervals and LTD only at negative ones. Our findings suggest that a single coincidence detector model based on the standard model of plasticity cannot account for order-specific STDP, and we predict that STDP requires two coincidence detectors.

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