Department of Electronics and Computer Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
Neural Comput. 2012 Oct;24(10):2604-54. doi: 10.1162/NECO_a_00335. Epub 2012 Jun 26.
Plasticity-inducing stimuli must typically be presented many times before synaptic plasticity is expressed, perhaps because induction signals gradually accumulate before overt strength changes occur. We consider memory dynamics in a mathematical model with synapses that integrate plasticity induction signals before expressing plasticity. We find that the memory trace initially rises before reaching a maximum and then falling. The memory signal dissociates into separate oblivescence and reminiscence components, with reminiscence initially dominating recall. In radical contrast, related but nonintegrative models exhibit only a highly problematic oblivescence. Synaptic integration mechanisms possess natural timescales, depending on the statistics of the induction signals. Together with neuromodulation, these timescales may therefore also begin to provide a natural account of the well-known spacing effect in the transition to late-phase plasticity. Finally, we propose experiments that could distinguish between integrative and nonintegrative synapses. Such experiments should further elucidate the synaptic signal processing mechanisms postulated by our model.
在突触可塑性表达之前，通常必须多次呈现诱导可塑性的刺激，这也许是因为在明显的强度变化发生之前，诱导信号逐渐积累。我们在一个数学模型中考虑记忆动力学，该模型中的突触在表达可塑性之前整合可塑性诱导信号。我们发现，记忆痕迹最初上升，然后达到最大值再下降。记忆信号分离成单独的遗忘和回忆成分，回忆最初占主导地位。与此形成鲜明对比的是，相关但非整合模型仅表现出高度有问题的遗忘。突触整合机制具有自然的时间尺度，这取决于诱导信号的统计特性。因此，与神经调制一起，这些时间尺度也可能开始为众所周知的长时程增强（LTP）过渡中的间隔效应提供自然解释。最后，我们提出了可以区分整合和非整合突触的实验。这些实验应该进一步阐明我们模型中假设的突触信号处理机制。
