Photonic implementation of spike timing dependent plasticity with weight-dependent learning window based on VCSOA

Laser Physics ◽  
2021 ◽  
Vol 32 (1) ◽  
pp. 016201
Author(s):  
Tao Tian ◽  
Zhengmao Wu ◽  
Xiaodong Lin ◽  
Xi Tang ◽  
Ziye Gao ◽  
...  
Keyword(s):  
Large Scale ◽  
Optical Amplifier ◽  
Bias Current ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Fabry Perot ◽  
The Stability ◽  
Dependent Learning ◽  
Vertical Cavity

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.

scholarly journals Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation

2015 ◽  
Vol 23 (19) ◽  
pp. 25247 ◽  
Author(s):  
Quansheng Ren ◽  
Yaolin Zhang ◽  
Rui Wang ◽  
Jianye Zhao
Keyword(s):  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Dependent Learning

Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity

2012 ◽  
Vol 24 (9) ◽  
pp. 2251-2279 ◽  
Author(s):  
Matthieu Gilson ◽  
Moritz Bürck ◽  
Anthony N. Burkitt ◽  
J. Leo van Hemmen
Keyword(s):  
Modulation Frequency ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Dendritic Location ◽  
Frequency Components ◽  
Frequency Identification ◽  
Specific Frequency ◽  
Dependent Learning ◽  
Dendritic Branches

Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filter with given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200 Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demonstrate how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification. We use an in-depth mathematical analysis of the learning dynamics in a population of plastic inhibitory connections. These provide inhomogeneous postsynaptic responses that depend on their dendritic location. We find that synaptic delays play a crucial role in organizing the weight specialization induced by STDP. Under suitable conditions on the synaptic delays and postsynaptic potentials (PSPs), the BMF of a neuron after learning can match the training frequency. In particular, proximal (distal) synapses with shorter (longer) dendritic delay and somatically measured PSP time constants respond better to higher (lower) frequencies. As a result, the neuron will respond maximally to any stimulating frequency (in a given range) with which it has been trained in an unsupervised manner. The model predicts that synapses responding to a given BMF form clusters on dendritic branches.

scholarly journals Memory Retention and Spike-Timing-Dependent Plasticity

2009 ◽  
Vol 101 (6) ◽  
pp. 2775-2788 ◽  
Author(s):  
Guy Billings ◽  
Mark C. W. van Rossum
Keyword(s):  
Retention Time ◽  
Single Neuron ◽  
Receptive Fields ◽  
Learning Rule ◽  
Spike Timing ◽  
Memory Retention ◽  
Neuron Models ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Dependent Learning

Memory systems should be plastic to allow for learning; however, they should also retain earlier memories. Here we explore how synaptic weights and memories are retained in models of single neurons and networks equipped with spike-timing-dependent plasticity. We show that for single neuron models, the precise learning rule has a strong effect on the memory retention time. In particular, a soft-bound, weight-dependent learning rule has a very short retention time as compared with a learning rule that is independent of the synaptic weights. Next, we explore how the retention time is reflected in receptive field stability in networks. As in the single neuron case, the weight-dependent learning rule yields less stable receptive fields than a weight-independent rule. However, receptive fields stabilize in the presence of sufficient lateral inhibition, demonstrating that plasticity in networks can be regulated by inhibition and suggesting a novel role for inhibition in neural circuits.

scholarly journals Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution

2007 ◽  
Vol 19 (3) ◽  
pp. 639-671 ◽  
Author(s):  
Taro Toyoizumi ◽  
Jean-Pascal Pfister ◽  
Kazuyuki Aihara ◽  
Wulfram Gerstner
Keyword(s):  
Optimality Criterion ◽  
Receptive Fields ◽  
Spike Timing ◽  
Postsynaptic Neuron ◽  
Spike Timing Dependent Plasticity ◽  
Basic Principles ◽  
Dependent Plasticity ◽  
The Mean ◽  
Synaptic Dynamics ◽  
The Stability

We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic processes stabilize the mean firing rate of the postsynaptic neuron, and (3) weak synapses adapt more slowly than strong ones, while maintenance of strong synapses is costly. Our results show that a synaptic update rule derived from these principles shares features, with spike-timing-dependent plasticity, is sensitive to correlations in the input and is useful for synaptic memory. Moreover, input selectivity (sharply tuned receptive fields) of postsynaptic neurons develops only if stimuli with strong features are presented. Sharply tuned neurons can coexist with unselective ones, and the distribution of synaptic weights can be unimodal or bimodal. The formulation of synaptic dynamics through an optimality criterion provides a simple graphical argument for the stability of synapses, necessary for synaptic memory.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

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