Dropout in Neural Networks Simulates theParadoxical Effects of Deep Brain Stimulation onMemory
Neuromodulation techniques such as Deep Brain Stimulation (DBS) are a promising treatment for memory-related disorders including anxiety, addiction, and dementia. However, the outcome of these treatments appears to be paradoxical, as the use of these techniques can both disrupt and enhance memory even when applied to the same brain target. In this paper, we hypothesize that disruption and enhancement of memory through neuromodulation can be explained by the dropout of engram nodes. We used a convolutional neural network to classify handwritten digits and letters, applying dropout at different stages to simulate DBS effects on engrams. We showed that dropout applied during training improves the accuracy of prediction, whereas dropout applied during testing dramatically decreases accuracy of prediction, which mimics enhancement and disruption of memory, respectively. We further showed that transfer learning of neural networks with dropout had increased accuracy and rate of learning. Dropout during training provided a more robust "skeleton" network where transfer learning can be applied, mimicking the effects of chronic DBS on memory. Overall, we show that dropout of nodes can be a potential mechanism by which neuromodulation techniques such as DBS can both disrupt and enhance memory and provides a unique perspective on this paradox.