Kim Daeyoung, Park Seongsik, Jeong YeonJoo, Kim Jaewook, Lee Suyoun, Kwak Joon Young, Jang Hyun Jae, Kim Inho, Kim Jong-Kook, Park Jongkil
IEEE Trans Neural Syst Rehabil Eng. 2025;33:2781-2792. doi: 10.1109/TNSRE.2025.3583057.
A deep understanding of neuronal circuitry connectivity is essential to replicate biological functions effectively. Inferring neural connectivity considers the cross-correlation of spike timing. Neuromorphic systems utilize online learning algorithms that leverage the temporal correlations of spikes by utilizing spiking neural networks. This research demonstrates real-time, large-scale neural connectivity inference by implementing the presynaptic spike-driven spike-timing-dependent plasticity method on neuromorphic hardware. We validate the capability of the proposed method to perform advanced neuron connectivity inference using synthetic data generated from leaky integrate-and-fire neurons on a multi-scale. Additionally, we analyzed that the proposed method exhibits invariant high inference performance in sparse networks without burst firing, regardless of transmission delay. Finally, we demonstrate the feasibility of the proposed method in real-time neural connectivity inference in actual in vitro or in vivo contexts by conducting neural connectivity inference simulating in a way closely mirroring in vitro conditions through fluorescence imaging signal data.
深入理解神经元回路的连通性对于有效复制生物功能至关重要。推断神经连通性需考虑尖峰时间的互相关性。神经形态系统利用在线学习算法，通过使用脉冲神经网络利用尖峰的时间相关性。本研究通过在神经形态硬件上实现突触前尖峰驱动的尖峰时间依赖性可塑性方法，展示了实时、大规模的神经连通性推断。我们使用从多尺度泄漏积分发放神经元生成的合成数据，验证了所提出方法执行高级神经元连通性推断的能力。此外，我们分析得出，所提出的方法在没有爆发式放电的稀疏网络中，无论传输延迟如何，都表现出不变的高推断性能。最后，我们通过荧光成像信号数据以紧密模拟体外条件的方式进行神经连通性推断模拟，证明了所提出方法在实际体外或体内环境中进行实时神经连通性推断的可行性。
