Devalle Federico, Roxin Alex
Computational Neuroscience Group, Centre de Recerca Matemàtica, Campus de Bellaterra, Edifici C, 08193, Bellterra, Spain.
J Comput Neurosci. 2025 Mar;53(1):9-23. doi: 10.1007/s10827-024-00885-z. Epub 2024 Dec 11.
Synaptic connections in neuronal circuits are modulated by pre- and post-synaptic spiking activity. Previous theoretical work has studied how such Hebbian plasticity rules shape network connectivity when firing rates are constant, or slowly varying in time. However, oscillations and fluctuations, which can arise through sensory inputs or intrinsic brain mechanisms, are ubiquitous in neuronal circuits. Here we study how oscillatory and fluctuating inputs shape recurrent network connectivity given a temporally asymmetric plasticity rule. We do this analytically using a separation of time scales approach for pairs of neurons, and then show that the analysis can be extended to understand the structure in large networks. In the case of oscillatory inputs, the resulting network structure is strongly affected by the phase relationship between drive to different neurons. In large networks, distributed phases tend to lead to hierarchical clustering. The analysis for stochastic inputs reveals a rich phase plane in which there is multistability between different possible connectivity motifs. Our results may be of relevance for understanding the effect of sensory-driven inputs, which are by nature time-varying, on synaptic plasticity, and hence on learning and memory.
神经元回路中的突触连接由突触前和突触后的发放活动调节。先前的理论研究探讨了在发放率恒定或随时间缓慢变化时，这种赫布可塑性规则如何塑造网络连接性。然而，通过感觉输入或内在脑机制产生的振荡和波动在神经元回路中普遍存在。在这里，我们研究在时间不对称可塑性规则下，振荡和波动输入如何塑造循环网络连接性。我们通过对神经元对采用时间尺度分离方法进行分析，然后表明该分析可扩展以理解大型网络的结构。在振荡输入的情况下，产生的网络结构受到驱动不同神经元的相位关系的强烈影响。在大型网络中，分布式相位往往导致分层聚类。对随机输入的分析揭示了一个丰富的相平面，其中不同可能的连接基序之间存在多重稳定性。我们的结果可能与理解本质上随时间变化的感觉驱动输入对突触可塑性以及学习和记忆的影响有关。
