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首页> 外文期刊>Neural computing & applications >Nonlinear behavior of memristive devices during tuning process and its impact on STDP learning rule in memristive neural networks
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Nonlinear behavior of memristive devices during tuning process and its impact on STDP learning rule in memristive neural networks

机译：忆阻器件在调谐过程中的非线性行为及其对忆阻神经网络中STDP学习规则的影响

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It is now widely accepted that memristive devices are promising candidates for the emulation of the behavior of biological synapses in neuromorphic systems. This is mainly because of the fact that like the strength of synapse, memristance of the memristive device can be tuned actively for example by the application of voltage or current. In addition, it is also possible to fabricate high density of memristive devices through the nano-crossbar structures. In this paper, we will show that there are some problems associated with memristive devices, which are playing the role of biological synapses. For example, we show that the variation rate of the memristance depends completely on the initial state of the device, and therefore, it can change significantly with time during the learning phase. This phenomenon can degrade the performance of learning methods like spike timing-dependent plasticity and cause the corresponding neuromorphic systems to become unstable. We also illustrate that using two serially connected memristive devices with different polarities as a synapse can somewhat fix the aforementioned problem.
机译：如今，忆阻器是模拟神经形态系统中生物突触行为的有前途的候选者。这主要是因为这样的事实，如突触的强度，忆阻器件的忆阻可以例如通过施加电压或电流来主动地进行调节。另外，还可以通过纳米纵横制结构制造高密度的忆阻器件。在本文中，我们将显示与忆阻器相关的一些问题，它们正在发挥生物突触的作用。例如，我们表明忆阻的变化率完全取决于设备的初始状态，因此，它在学习阶段可能会随时间而显着变化。这种现象可能会降低学习方法的性能，例如与峰值时序相关的可塑性，并使相应的神经形态系统变得不稳定。我们还说明了使用两个极性不同的串联忆阻器件作为突触可以在某种程度上解决上述问题。

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