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....
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although bio-plausible algorithms have been developed for deep networks, these algorithms are typically tested and developed for offline settings (e.g., refs.8,9,10,11). Although progress has been made on online-continual learning, essentially all of this work ...
In the 20th century we thought the brain extracted knowledge from sensations. The 21st century witnessed a ‘strange inversion’, in which the brain became an organ of inference, actively constructing explanations for what’s going on ‘out there’, beyo
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...
Adult semantic memory has been traditionally conceptualized as a relatively static memory system that consists of knowledge about the world, concepts, and
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...
stimuli in the literature. SeeSupplementary Informationfor details of the VAE’s architecture. The VAEs were trained using gradient descent and back-propagation as usual; while this method is biologically implausible due to its non-local nature, more plausible learning algorithms might be feasible143...
Using these analogous tasks, we found analogous valuation of information by humans, monkeys and neurons. We first assessed the fundamentals of how individuals and neurons valued Info versus Noinfo offers and investigated the algorithms they used to compute these values. In humans, many individuals wer...