scholarly journals Is a 4-Bit Synaptic Weight Resolution Enough? – Constraints on Enabling Spike-Timing Dependent Plasticity in Neuromorphic Hardware

2012 ◽  
Vol 6 ◽  
Author(s):  
Thomas Pfeil ◽  
Tobias C. Potjans ◽  
Sven Schrader ◽  
Wiebke Potjans ◽  
Johannes Schemmel ◽  
...  
Keyword(s):  
Synaptic Weight ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neuromorphic Hardware

A Calcium-Based Simple Model of Multiple Spike Interactions in Spike-Timing-Dependent Plasticity

2013 ◽  
Vol 25 (7) ◽  
pp. 1853-1869 ◽  
Author(s):  
Takumi Uramoto ◽  
Hiroyuki Torikai
Keyword(s):  
Synaptic Weight ◽  
Spike Timing ◽  
State Variables ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Synaptic Modification ◽  
Proposed Model ◽  
Layer 2 ◽  
Visual Cortical ◽  
Cortical Slices

Spike-timing-dependent plasticity (STDP) is a form of synaptic modification that depends on the relative timings of presynaptic and postsynaptic spikes. In this letter, we proposed a calcium-based simple STDP model, described by an ordinary differential equation having only three state variables: one represents the density of intracellular calcium, one represents a fraction of open state NMDARs, and one represents the synaptic weight. We shown that in spite of its simplicity, the model can reproduce the properties of the plasticity that have been experimentally measured in various brain areas (e.g., layer 2/3 and 5 visual cortical slices, hippocampal cultures, and layer 2/3 somatosensory cortical slices) with respect to various patterns of presynaptic and postsynaptic spikes. In addition, comparisons with other STDP models are made, and the significance and advantages of the proposed model are discussed.

scholarly journals Stochastic Learning in Neuromorphic Hardware via Spike Timing Dependent Plasticity With RRAM Synapses

2018 ◽  
Vol 8 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Giacomo Pedretti ◽  
Valerio Milo ◽  
Stefano Ambrogio ◽  
Roberto Carboni ◽  
Stefano Bianchi ◽  
...  
Keyword(s):  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Stochastic Learning ◽  
Neuromorphic Hardware

Soft-Reward Based Reinforcement Learning by Spiking Neural Networks

2011 ◽  
Vol 219-220 ◽  
pp. 770-773
Author(s):  
Wei Ya Shi
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Synaptic Weight ◽  
Spike Timing ◽  
Benchmark Dataset ◽  
Spiking Neural Networks ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Winner Take All ◽  
Take All

In this paper, we propose algorithm based reinforcement learning for spiking neural networks. The algorithm simulates biological adaptability and uses the soft-reward from environment to modulate the synaptic weight, which combines spike-timing-dependent plasticity (STDP), winner-take-all mechanism. The algorithm is tested to classify a number of standard benchmark dataset. The obtained results show the effectiveness of the proposed algorithm.

scholarly journals Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoshifumi Nishi ◽  
Kumiko Nomura ◽  
Takao Marukame ◽  
Koichi Mizushima
Keyword(s):  
Unsupervised Learning ◽  
Recognition Accuracy ◽  
Learning Rule ◽  
Spike Timing ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Cumulative Probability ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Neuromorphic Hardware

AbstractSpike timing-dependent plasticity (STDP), which is widely studied as a fundamental synaptic update rule for neuromorphic hardware, requires precise control of continuous weights. From the viewpoint of hardware implementation, a simplified update rule is desirable. Although simplified STDP with stochastic binary synapses was proposed previously, we find that it leads to degradation of memory maintenance during learning, which is unfavourable for unsupervised online learning. In this work, we propose a stochastic binary synaptic model where the cumulative probability of the weight change evolves in a sigmoidal fashion with potentiation or depression trials, which can be implemented using a pair of switching devices consisting of serially connected multiple binary memristors. As a benchmark test we perform simulations of unsupervised learning of MNIST images with a two-layer network and show that simplified STDP in combination with this model can outperform conventional rules with continuous weights not only in memory maintenance but also in recognition accuracy. Our method achieves 97.3% in recognition accuracy, which is higher than that reported with standard STDP in the same framework. We also show that the high performance of our learning rule is robust against device-to-device variability of the memristor's probabilistic behaviour.

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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