The Double Vector Quantization (DVQ) method, a long-term forecasting method based on the self-organizing maps algorithm, has been used to predict the 100 missing values of the CAT...
Geoffroy Simon, John Aldo Lee, Marie Cottrell, Mic...
Researchers studying word learning have discovered that the syntactic frame in which a word appears plays an important role in the interpretation of the word, and this importance ...
This perspective paper explores principles of unsupervised learning and how they relate to face recognition. Dependency coding and information maximization appear to be central pr...
Over the course of the first few months of life, our brains accomplish a remarkable feat. They are able to interpret complex visual images so that instead of being just disconnec...
In this paper we present a method of parameter optimization, relative trust-region learning, where the trust-region method and the relative optimization [21] are jointly exploited...
This paper presents a computational theory of developmental mental architectures for artificial and natural systems, motivated by neuroscience. The work is an attempt to approxim...
A realistic model of activity dependent dynamical synapses is used to study the conditions in which a postsynaptic neuron detects temporal coincidences of spikes arriving from N d...
A quantitative model of auditory learning is presented to predict how auditory patterns are stored in the songbird auditory forebrain. This research focuses on the caudomedial nid...
Patrick D. Roberts, Roberto A. Santiago, Tarciso V...
Many conceptual studies of local cortical networks assume completely random wiring. For spatially extended networks, however, such random graph models are inadequate. The geometry...