Computing with the leaky integrate-and-fire neuron: Logarithmic computation and multiplication
Computing with the leaky integrate-and-fire neuron: Logarithmic computation and multiplication
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DOI：
10.1162/neco.1997.9.2.305
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发表时间：
1997-02-15
影响因子：
2.9
通讯作者：
Schwartz, EL
中科院分区：
文献类型：
--
作者：
Tal, D;Schwartz, EL
神经元尖峰发放的泄漏积分和激发（LIF）模型（Stein 1967）提供了一个易于分析的形式主义的神经元放电率在神经元的膜时间常数，阈值，和不应期。LIF神经元主要用于模拟生理上真实的尖峰序列，但LIF模型似乎很少应用于明确的计算环境。在这篇文章中，我们表明，一个LIF神经元的传递函数提供了，在一个宽的参数范围内，压缩非线性足够接近的对数，使LIF神经元可以被用来乘以神经信号，仅仅增加他们的输出产生的对数的产品。LIF乘法器的仿真表明，在广泛的参数选择下，LIF神经元可以将其输入对数相乘，相对误差在5%以内。
The leaky integrate-and-fire (LIF) model of neuronal spiking (Stein 1967) provides an analytically tractable formalism of neuronal firing rate in terms of a neuron's membrane time constant, threshold, and refractory period. LIF neurons have mainly been used to model physiologically realistic spike trains, but little application of the LIF model appears to have been made in explicitly computational contexts. In this article, we show that the transfer function of a LIF neuron provides, over a wide-parameter range, a compressive nonlinearity sufficiently close to that of the logarithm so that LIF neurons can be used to multiply neural signals by mere addition of their outputs yielding the logarithm of the product. A simulation of the LIF multiplier shows that under a wide choice of parameters, a LIF neuron can log-multiply its inputs to within a 5% relative error.