Imperial College London, London, United Kingdom.
University of Bern, Bern, Switzerland.
Sci Rep. 2023 Apr 21;13(1):6543. doi: 10.1038/s41598-023-32410-0.
With Hebbian learning 'who fires together wires together', well-known problems arise. Hebbian plasticity can cause unstable network dynamics and overwrite stored memories. Because the known homeostatic plasticity mechanisms tend to be too slow to combat unstable dynamics, it has been proposed that plasticity must be highly gated and synaptic strengths limited. While solving the issue of stability, gating and limiting plasticity does not solve the stability-plasticity dilemma. We propose that dendrites enable both stable network dynamics and considerable synaptic changes, as they allow the gating of plasticity in a compartment-specific manner. We investigate how gating plasticity influences network stability in plastic balanced spiking networks of neurons with dendrites. We compare how different ways to gate plasticity, namely via modulating excitability, learning rate, and inhibition increase stability. We investigate how dendritic versus perisomatic gating allows for different amounts of weight changes in stable networks. We suggest that the compartmentalisation of pyramidal cells enables dendritic synaptic changes while maintaining stability. We show that the coupling between dendrite and soma is critical for the plasticity-stability trade-off. Finally, we show that spatially restricted plasticity additionally improves stability.
随着赫布学习“一起发射的神经元连接在一起”,出现了一些众所周知的问题。赫布可塑性可能导致不稳定的网络动态和覆盖存储的记忆。由于已知的同型稳态可塑性机制往往太慢而无法对抗不稳定的动态,因此有人提出可塑性必须受到高度门控和突触强度限制。虽然门控和限制可塑性解决了稳定性问题,但并没有解决稳定性-可塑性困境。我们提出,树突使稳定的网络动态和相当大的突触变化成为可能,因为它们允许以特定于隔室的方式门控可塑性。我们研究了在具有树突的神经元的可塑性平衡尖峰网络中,门控可塑性如何影响网络稳定性。我们比较了通过调节兴奋性、学习率和抑制来门控可塑性的不同方式如何增加稳定性。我们研究了树突和胞体门控如何允许在稳定网络中进行不同数量的权重变化。我们认为,金字塔细胞的隔室化使树突突触变化成为可能,同时保持稳定性。我们表明,树突和胞体之间的耦合对于可塑性-稳定性权衡至关重要。最后,我们表明,空间限制的可塑性还可以提高稳定性。