Title: Weight transport through spike timing for robust local gradients Show Chinese title

Title: 通过尖峰时间进行权重传输以获得稳健的局部梯度

Abstract: In both machine learning and in computational neuroscience, plasticity in functional neural networks is frequently expressed as gradient descent on a cost. Often, this imposes symmetry constraints that are difficult to reconcile with local computation, as is required for biological networks or neuromorphic hardware. For example, wake-sleep learning in networks characterized by Boltzmann distributions builds on the assumption of symmetric connectivity. Similarly, the error backpropagation algorithm is notoriously plagued by the weight transport problem between the representation and the error stream. Existing solutions such as feedback alignment tend to circumvent the problem by deferring to the robustness of these algorithms to weight asymmetry. However, they are known to scale poorly with network size and depth. We introduce spike-based alignment learning (SAL), a complementary learning rule for spiking neural networks, which uses spike timing statistics to extract and correct the asymmetry between effective reciprocal connections. Apart from being spike-based and fully local, our proposed mechanism takes advantage of noise. Based on an interplay between Hebbian and anti-Hebbian plasticity, synapses can thereby recover the true local gradient. This also alleviates discrepancies that arise from neuron and synapse variability -- an omnipresent property of physical neuronal networks. We demonstrate the efficacy of our mechanism using different spiking network models. First, we show how SAL can significantly improve convergence to the target distribution in probabilistic spiking networks as compared to Hebbian plasticity alone. Second, in neuronal hierarchies based on cortical microcircuits, we show how our proposed mechanism effectively enables the alignment of feedback weights to the forward pathway, thus allowing the backpropagation of correct feedback errors. Show Chinese abstract

Abstract: 在机器学习和计算神经科学中，功能神经网络中的可塑性通常表现为对成本的梯度下降。 通常，这会施加对称性约束，这很难与局部计算相协调，正如生物网络或类脑硬件所要求的那样。 例如，由玻尔兹曼分布表征的网络中的清醒-睡眠学习建立在连接对称性的假设之上。 同样，误差反向传播算法因表示和误差流之间的权重传输问题而臭名昭著。 现有的解决方案，如反馈对齐，倾向于通过依赖这些算法对权重不对称的鲁棒性来绕过这个问题。 然而，它们已知在网络规模和深度上扩展效果不佳。 我们引入了基于脉冲的对齐学习（SAL），这是一种用于脉冲神经网络的互补学习规则，它利用脉冲时间统计量来提取并校正有效互连之间的不对称性。 除了基于脉冲且完全局部外，我们提出的机制还利用了噪声。 基于赫布和反赫布可塑性的相互作用，突触可以恢复真实的局部梯度。 这也缓解了由于神经元和突触变异性产生的差异——这是物理神经网络的一个普遍特性。 我们使用不同的脉冲网络模型证明了我们机制的有效性。 首先，我们展示了与单独的赫布可塑性相比，SAL如何显著提高概率脉冲网络收敛到目标分布的效果。 其次，在基于皮层微电路的神经层次结构中，我们展示了我们提出的机制如何有效地使反馈权重与前向路径对齐，从而允许正确反馈误差的反向传播。