The brain can learn new tasks without forgetting old ones. This memory retention is closely associated with the long-term stability of synaptic strength. To understand the capacity of pyramidal neurons to preserve memory under different tasks, we established a plasticity model based on the postsynaptic membrane energy state, in which the change in synaptic strength depends on the difference between the energy state after stimulation and the resting energy state. If the post-stimulation energy state is higher than the resting energy state, then synaptic depression occurs. On the contrary, the synapse is strengthened. Our model unifies homo- and heterosynaptic plasticity and can reproduce synaptic plasticity observed in multiple experiments, such as spike-timing-dependent plasticity, and cooperative plasticity with few and common parameters. Based on the proposed plasticity model, we conducted a simulation study on how the activation patterns of dendritic branches by different tasks affect the synaptic connection strength of pyramidal neurons. We further investigate the formation mechanism by which different tasks activate different dendritic branches. Simulation results show that compare to the classic plasticity model, the plasticity model we proposed can achieve a better spatial separation of different branches activated by different tasks in pyramidal neurons, which deepens our insight into the memory retention mechanism of brains.

大脑可以学习新任务而不会忘记旧任务。这种记忆保留与突触强度的长期稳定性密切相关。为了了解锥体神经元在不同任务下保存记忆的能力，我们建立了一个基于突触后膜能量状态的可塑性模型，其中突触强度的变化取决于刺激后能量状态与静息能量状态之间的差异。如果刺激后能量状态高于静息能量状态，则发生突触抑制。相反，突触得到加强。我们的模型统一了同型和异型突触可塑性，并且可以重现在多个实验中观察到的突触可塑性，例如尖峰时间依赖性可塑性，以及具有很少和通用参数的协同可塑性。基于所提出的可塑性模型，我们对不同任务对树突分支的激活模式如何影响锥体神经元的突触连接强度进行了模拟研究。我们进一步研究了不同任务激活不同树突分支的形成机制。仿真结果表明，与经典的可塑性模型相比，我们提出的可塑性模型可以更好地实现锥体神经元中不同任务激活的不同分支的空间分离，加深了我们对大脑记忆保留机制的理解。我们进一步研究了不同任务激活不同树突分支的形成机制。仿真结果表明，与经典的可塑性模型相比，我们提出的可塑性模型可以更好地实现锥体神经元中不同任务激活的不同分支的空间分离，加深了我们对大脑记忆保留机制的理解。我们进一步研究了不同任务激活不同树突分支的形成机制。仿真结果表明，与经典的可塑性模型相比，我们提出的可塑性模型可以更好地实现锥体神经元中不同任务激活的不同分支的空间分离，加深了我们对大脑记忆保留机制的理解。