Biometaphorical Computing, Computational Biology Center, IBM Research Division, IBM T. J. Watson Research Center Yorktown Heights, NY, USA.
Front Neural Circuits. 2010 Mar 10;4:7. doi: 10.3389/fncir.2010.00007. eCollection 2010.
We show that the local spike timing-dependent plasticity (STDP) rule has the effect of regulating the trans-synaptic weights of loops of any length within a simulated network of neurons. We show that depending on STDP's polarity, functional loops are formed or eliminated in networks driven to normal spiking conditions by random, partially correlated inputs, where functional loops comprise synaptic weights that exceed a positive threshold. We further prove that STDP is a form of loop-regulating plasticity for the case of a linear network driven by noise. Thus a notable local synaptic learning rule makes a specific prediction about synapses in the brain in which standard STDP is present: that under normal spiking conditions, they should participate in predominantly feed-forward connections at all scales. Our model implies that any deviations from this prediction would require a substantial modification to the hypothesized role for standard STDP. Given its widespread occurrence in the brain, we predict that STDP could also regulate long range functional loops among individual neurons across all brain scales, up to, and including, the scale of global brain network topology.
我们证明，局部尖峰时间依赖可塑性（STDP）规则具有调节神经元模拟网络中任何长度的回路的跨突触权重的作用。我们证明，取决于 STDP 的极性，在由随机、部分相关输入驱动到正常尖峰条件的网络中，功能回路被形成或消除，其中功能回路包含超过正阈值的突触权重。我们进一步证明，对于由噪声驱动的线性网络，STDP 是一种回路调节可塑性的形式。因此，一个显著的局部突触学习规则对存在标准 STDP 的大脑中的突触做出了一个具体的预测：在正常的尖峰条件下，它们应该在所有尺度上主要参与前馈连接。我们的模型表明，任何偏离这一预测的情况都需要对标准 STDP 的假设作用进行实质性的修改。鉴于其在大脑中的广泛存在，我们预测 STDP 也可以调节个体神经元之间跨越所有大脑尺度的长程功能回路，包括全局大脑网络拓扑的尺度。
