Unsupervised Learning through Prediction in a Model of Cortex
We propose a primitive called PJOIN, for "predictive join," which combines and extends the operations JOIN and LINK, which Valiant proposed as the basis of a computational theory of cortex. We show that PJOIN can be implemented in Valiant's model. We also show that, using PJOIN, certain reasonably complex learning and pattern matching tasks can be performed, in a way that involves phenomena which have been observed in cognition and the brain, namely memory-based prediction and downward traffic in the cortical hierarchy.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Fundamental principles of cortical computation: unsupervised learning with prediction, compression and feedback
There has been great progress in understanding of anatomical and functional microcircuitry of the primate cortex. However, the fundamental principles of cortical computation - the principles that allow the visual cortex …
Visual TrackingMechanisms for spontaneous symmetry breaking in developing visual cortex
For the brain to recognize local orientations within images, neurons must spontaneously break the translation and rotation symmetry of their response functions -- an archetypal example of unsupervised learning. The domin…
CORTEX: Token-Level Hallucination Detection in RAG via Comparative Internal Representations
In this paper, we propose CORTEX, a token-level hallucination detection method for Retrieval-Augmented Generation (RAG). In long-form RAG outputs, hallucinations often arise in localized spans rather than throughout an e…
Characterization of Visual Object Representations in Rat Primary Visual Cortex
For most animal species, quick and reliable identification of visual objects is critical for survival. This applies also to rodents, which, in recent years, have become increasingly popular models of visual functions. Fo…
ClusteringGeneral ClassificationPositionProbing neural representations of scene perception in a hippocampally dependent task using artificial neural networks
Deep artificial neural networks (DNNs) trained through backpropagation provide effective models of the mammalian visual system, accurately capturing the hierarchy of neural responses through primary visual cortex to infe…
HippocampusSemantic SegmentationTripletUnsupervised Object Segmentation