Soldado-Magraner Saray, Sorbaro Martino, Laje Rodrigo, Buonomano Dean V, Indiveri Giacomo
Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
Department of Neurobiology, University of California, Los Angeles, CA, USA.
Nat Commun. 2025 Jul 1;16(1):5522. doi: 10.1038/s41467-025-60697-2.
Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits represent a promising approach for implementing the brain's computational primitives. However, achieving the same robustness of biological networks in neuromorphic systems remains a challenge due to the variability in their analog components. Inspired by real cortical networks, we apply a biologically-plausible cross-homeostatic rule to balance neuromorphic implementations of spiking recurrent networks. We demonstrate how this rule can autonomously tune the network to produce robust, self-sustained dynamics in an inhibition-stabilized regime, even in presence of device mismatch. It can implement multiple, co-existing stable memories, with emergent soft-winner-take-all and reproduce the "paradoxical effect" observed in cortical circuits. In addition to validating neuroscience models on a substrate sharing many similar limitations with biological systems, this enables the automatic configuration of ultra-low power, mixed-signal neuromorphic technologies despite the large chip-to-chip variability.
许多神经计算源自循环神经回路中自我维持的活动模式，这些回路依赖于平衡的兴奋和抑制。神经形态电子电路是实现大脑计算原语的一种很有前景的方法。然而，由于其模拟组件的变异性，在神经形态系统中实现与生物网络相同的稳健性仍然是一个挑战。受真实皮层网络的启发，我们应用一种生物学上合理的交叉稳态规则来平衡脉冲循环网络的神经形态实现。我们展示了这条规则如何能够自动调整网络，以便在抑制稳定状态下产生稳健的、自我维持的动态，即使存在器件失配。它可以实现多个共存的稳定记忆，具有涌现的软胜者全得特性，并重现皮层回路中观察到的“悖论效应”。除了在与生物系统有许多相似限制的基板上验证神经科学模型之外，这还能够实现超低功耗、混合信号神经形态技术的自动配置，尽管芯片与芯片之间存在很大的变异性。
