Lateral inhibition models coupled with Hebbian plasticity have been shown to learn factorised causal representations of input stimuli, for instance, oriented edges are learned from natural images. Currently, these models require the recurrent dynamics to settle into a stable state before weight changes can be applied, which is not only biologically implausible, but also impractical for real-time learning systems. Here, we propose a new Hebbian learning rule which is implemented using plausible biological mechanisms that have been observed experimentally. We find that this rule allows for efficient, time-continuous learning of factorised representations, very similar to the classic noncontinuous Hebbian/anti-Hebbian learning. Furthermore, we show that this rule naturally prevents catastrophic forgetting when stimuli from different distributions are shown sequentially.
A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination
Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales
Department of Information and Electric Engineering and Applied Mathematics, University of Salerno, Italy（信息与电气工程及应用数学系，萨勒诺大学）；Center for Control, Dynamical Systems, and Computation, UC Santa Barbara, CA, USA（控制、动态系统与计算中心，加州大学圣芭芭拉分校）
Noise-induced synchronization and anti-resonance in excitable systems; Implications for information processing in Parkinson's Disease and Deep Brain Stimulation
