Spratling MW. A review of predictive coding algorithms. Brain Cogn. 2016 Jan;Epub ahead of print doi: 10.1016/j.bandc.2015.11.003 PMID: 26809759Spratling, M. W. (2016). A review of predictive coding algorithms. Brain Cogn. doi: 10.1016/j.bandc.2015.11.003 [Epub ahead of print].M.W....
In Section 4, we review computational approaches motivated by biological aspects of learning which include critical developmental stages and curriculum learning (Section 4.2), transfer learning for the reuse of knowledge during the learning of new tasks (Section 4.3), reinforcement learning for the ...
Within a predictive coding framework, the dual aspect role of the hippocampus gives rise to two complementary hippocampal-neocortical interactions. Descending inputs from the hippocampus are shown in blue. An example subset of cells in the neocortex are shown in the black box with low firing rate...
A non-exhaustive, but useful taxonomy of algorithms in modern Model-Based RL. We simply divide Model-Based RL into two categories: Learn the Model and Given the Model. Learn the Model mainly focuses on how to build the environment model. Given the Model cares about how to utilize the learn...
The invention relates to a coding system predictive of a digital code speech signal. The coded digital signal (Sn) is formed by a coded speech signal and, if necessary, by auxiliary data. A perceptual weighting filter (11) is formed by a short-term prediction filter of the speech signal ...
Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.[paper] A review paper about the potential of deep learning for multi-omics data integration. Protein biology This category is divided into sub-categories. ...
Ethics approval was acquired from the institutional review boards of all participating institutions, and written informed consent was obtained from all ADNI participants at enrollment. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests.Additio...
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the b
This survey aims to investigate and present a thorough review of the most popular and effective anomaly detection techniques applied to detect financial fraud, with a focus on highlighting the recent advancements in the areas of semi-supervised and unsupervised learning....
Deep learning is a subset of machine learning. It is responsible for many of the awe-inspiring news stories about AI in the news (e.g., self-driving cars, ChatGPT). Deep learning algorithms are inspired by the brain's structure and work exceptionally well with unstructured data such as im...