输入作为脉冲序列接收,每个输入神经元一个,从输入数据生成,例如通过泊松或群体编码。 3.3. Surrogate Gradient Learning 为了训练模型,我们实现了一种替代梯度学习规则,称为事件驱动的随机反向传播(eRBP) (Neftci et al., 2017)。eRBP依赖于突触前发放、突触后替代梯度和通过固定随机权重的误差反馈,近似反向传播。在eRB...
[8] Aljundi R. Continual learning in neural networks[J]. arXiv preprint arXiv:1910.02718, 2019.
is a key feature of natural intelligence, but an unsolved problem in deep learning. Particularly challenging for deep neural networks is ‘class-incremental learning’, whereby a network must learn to distinguish between classes that are not observed together. In this guest lecture, I will ...
Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architectures. Previous approaches to the problem have considered either the progressive increase in the size of the networks, or...
of previously seen data, a problem that neural networks are well known to suffer from. The work described in this thesis has been dedicated to the investigation of continual learning and solutions to mitigate the forgetting phenomena in neural networks. To approach the continual learning problem, ...
Just as continual learning in humans cannot be reduced to a single biological mechanism, but is rather the product of multiple systems that range from the synaptic plasticity of single neurons to the entire memory system, continual learning in artificial neural networks is not trivial to implement ...
Computer programs that learn to perform tasks also typically forget them very quickly. We show that the learning rule can be modified so that a program can remember old tasks when learning a new...
3.4. Continual Learning with eRBP 我们目前工作的目标是仅使用单个神经元和突触中局部可用的信息来保存突触知识。为了实现这一点,我们引入了神经启发的机制,以保留突触知识并增加突触本身的复杂性,如图2所示。 第一种机制,活动依赖的元可塑性,是对Laborieux et al. (2020)描述的技术的改编,其中突触的可塑性取决于...
Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architectures. Previous approaches to the problem have considered either the progressive increase in the size of the networks, or...
In Section 2, we introduce a set of widely studied biological aspects of lifelong learning and their implications for the modelling of biologically motivated neural network architectures. First, we focus on the mechanisms of neurosynaptic plasticity that regulate the stability–plasticity balance in ...