Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
1994 Special Issue: A model of hippocampal function
Neural Networks - Special issue: models of neurodynamics and behavior
TD(λ) Converges with Probability 1
Machine Learning
Learning and communication via imitation: an autonomous robot perspective
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
A biologically inspired integrated model of different hippocampal subsystems makes a distinction between place cells (PC) within entorhinal cortex (diffuse) or dentate gyrus (segregated), and transition cells (TC) in CA3-CA1 that encode transitions between events. These two types of codes support two kinds of hippocampocortical cognitive maps: -A context-independent map in subiculum and EC encodes essentially the spatial layout of the environment thanks to a local dominance of ideothetic movement-related information over allothetic (visual) information; -A task-and-temporal-context dependent map based on the TCs in CA3-CA1 allows encoding, in higher order structures, maps as graphs resulting from combination of learned sequences of events. The dominantly spatial and the temporal-task-dependent maps are permanently stored in parietal cortex and prefrontal cortex respectively. On the basis of these two maps two distinct goal-oriented navigation strategies were designed in experimental robotic paradigms: -one based on a (population) vector code of the location-actions pairs to learn and implement to reach the goal; another based on linking TCs together as conditioning chains that will be implemented under the top-down guidance of drives and motivations.