Understanding the pathogenesis of epilepsy including changes in synaptic pathways can improve our knowledge about epilepsy and development of new treatments. In this regard, data-driven models such as artificial neural networks, which are able to capture the effects of synaptic plasticity, can play an important role. This paper proposes long short term memory (LSTM) as the ideal architecture for modeling plasticity changes, and validates this proposal via experimental data. As a special class of recurrent neural networks (RNNs), LSTM is able to track information through time and control its flow via several gating mechanisms, which allow for maintaining the relevant and forgetting the irrelevant information. In our experiments, potentiation and depotentiation of motor circuit and perforant pathway as two forms of plasticity were respectively induced by kindled and kindled + transcranial magnetic stimulation of animal groups. In kindling, both procedure duration and gradual synaptic changes play critical roles. The stimulation of both groups continued for six days. Both after-discharge (AD) and seizure behavior as two biologically measurable effects of plasticity were recorded immediately post each stimulation. Three classes of artificial neural networks—LSTM, RNN, and feedforward neural network (FFNN)—were trained to predict AD and seizure behavior as indicators of plasticity during these six days. Results obtained from the collected data confirm the superiority of LSTM. For seizure behavior, the prediction accuracies achieved by these three models were 0.91 ± 0.01, 0.77 ± 0.02, and 0.59 ± 0.02%, respectively, and for AD, the prediction accuracies were 0.82 ± 0.01, 0.74 ± 0.08 and 0.42 ± 0.1, respectively.

了解癫痫的发病机制，包括突触通路的变化，可以提高我们对癫痫和新疗法开发的认识。在这方面，能够捕捉突触可塑性影响的人工神经网络等数据驱动模型可以发挥重要作用。本文提出长短期记忆 (LSTM) 作为可塑性变化建模的理想架构，并通过实验数据验证了该提议。作为一类特殊的递归神经网络 (RNN)，LSTM 能够随时间跟踪信息并通过多种门控机制控制信息流，从而保持相关信息并忘记无关信息。在我们的实验中，点燃和点燃+经颅磁刺激动物组分别诱导运动回路和穿孔通路的增强和去电位作为可塑性的两种形式。在点燃过程中，过程持续时间和渐进的突触变化都起着至关重要的作用。两组的刺激都持续了六天。每次刺激后立即记录放电后 (AD) 和癫痫发作行为这两种生物学上可测量的可塑性效应。三类人工神经网络——LSTM、RNN 和前馈神经网络 (FFNN)——被训练来预测 AD 和癫痫发作行为，作为这六天内的可塑性指标。从收集的数据中获得的结果证实了 LSTM 的优越性。对于癫痫发作行为，这三个模型的预测准确度分别为 0.91 ± 0.01、0。