Continual learning in computational systems is challenging due to catastrophic forgetting. We discovered a two layer neural circuit in the fruit fly olfactory system that addresses this challenge by uniquely combining sparse coding and associative learning. In the first layer, odors are encoded using sparse, high dimensional representations, which reduces memory interference by activating non overlapping populations of neurons for different odors. In the second layer, only the synapses between odor activated neurons and the output neuron associated with the odor are modified during learning; the rest of the weights are frozen to prevent unrelated memories from being overwritten. We show empirically and analytically that this simple and lightweight algorithm significantly boosts continual learning performance. The fly associative learning algorithm is strikingly similar to the classic perceptron learning algorithm, albeit two modifications, which we show are critical for reducing catastrophic forgetting. Overall, fruit flies evolved an efficient lifelong learning algorithm, and circuit mechanisms from neuroscience can be translated to improve machine computation.
翻译:计算系统中的持续学习由于灾难性的遗忘而具有挑战性。 我们在果蝇嗅觉系统中发现了两层神经电路,通过将稀疏的编码和关联性学习的独特结合来应对这一挑战。 在第一层,通过稀疏的、高维的表达方式,对水体进行编码,通过激活不同气态的神经非重叠成份来减少记忆干扰。在第二层,只有气味活性神经元和与气味相关的输出神经神经的突触在学习过程中得到修改;其余的重量被冻结,以防止不相干记忆被过度书写。我们从经验上和分析上表明,这种简单和轻量级的算法极大地提升了持续学习的绩效。飞行关联性学习算法与典型的感官学习算法非常相似,尽管我们已表明,两种修改对于减少灾难性的遗忘至关重要。总的来说,水果苍蝇演变出一种高效的终身学习算法,而神经科学的电路机制可以转换为改进机器的计算方法。