首页>
外国专利>
Artificial neural networks based on a low-order model of biological neural networks
Artificial neural networks based on a low-order model of biological neural networks
展开▼
机译:基于生物神经网络低阶模型的人工神经网络
展开▼
页面导航
摘要
著录项
相似文献
摘要
A low-order model (LOM) of biological neural networks and its mathematical equivalents including the clusterer interpreter probabilistic associative memory (CIPAM) are disclosed. They are artificial neural networks (ANNs) organized as networks of processing units (PUs), Each PU comprising artificial neuronal encoders, synapses, spiking/nonspiking neurons, and a scheme for maximal generalization. If the weights in the artificial synapses in a PU have been learned (and then fixed) or can be adjusted by the unsupervised accumulation rule and the unsupervised covariance rule (or supervised covariance rule), the PU is called unsupervised (or supervised) PU. The disclosed ANNs, with these Hebbian-type learning rules, can learn large numbers of large input vectors with temporally/spatially hierarchical causes with ease and recognize such causes with maximal generalization despite corruption, distortion and occlusion. An ANN with a network of unsupervised PUs (called clusterer) and offshoot supervised PUs (called interpreter) is an architecture for many applications.
展开▼