Neural computation
Oct 20, 2020
Backpropagation (BP) is the cornerstone of today's deep learning algorithms, but it is inefficient partially because of backward locking, which means updating the weights of one layer locks the weight updates in the other layers. Consequently, it is ...
Neural computation
Oct 20, 2020
Working memory is essential: it serves to guide intelligent behavior of humans and nonhuman primates when task-relevant stimuli are no longer present to the senses. Moreover, complex tasks often require that multiple working memory representations ca...
Neural computation
Oct 20, 2020
Nonlinear interactions in the dendritic tree play a key role in neural computation. Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks, such as the Neural Engineering Framework, tend to assu...
Neural computation
Oct 20, 2020
Recent advances in weakly supervised classification allow us to train a classifier from only positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the class-prior probability, a critic...
Neural computation
Oct 20, 2020
The ability to encode and manipulate data structures with distributed neural representations could qualitatively enhance the capabilities of traditional neural networks by supporting rule-based symbolic reasoning, a central property of cognition. Her...
Neural computation
Oct 20, 2020
Pruning is an effective way to slim and speed up convolutional neural networks. Generally previous work directly pruned neural networks in the original feature space without considering the correlation of neurons. We argue that such a way of pruning ...
Neural computation
Oct 20, 2020
We develop theoretical foundations of resonator networks, a new type of recurrent neural network introduced in Frady, Kent, Olshausen, and Sommer (2020), a companion article in this issue, to solve a high-dimensional vector factorization problem aris...
Medical & biological engineering & computing
Oct 20, 2020
In the present paper, deep convolutional neural network (DCNN) is applied to multilocus protein subcellular localization as it is more suitable for multi-class classification. There are two main problems with this application. First, the appropriate ...
Medical physics
Oct 20, 2020
PURPOSE: In medical image analysis, deep learning has great application potential. Discovering a method for extracting valuable information from medical images and integrating that information closely with medical treatment has recently become a majo...
Advanced materials (Deerfield Beach, Fla.)
Oct 20, 2020
Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time- and energy-efficient computational paradigms for the Internet-of-Things and edge ...