Cellular topographic self-organization under correlational learning
نویسندگان
چکیده
We consider two layered binary state neural networks in which cellular topographic self-organization occurs under correlational learning. The main result is that for separable input relations, a mapping is topographic if it is stable and vice versa.
منابع مشابه
S-Map: A Network with a Simple Self-Organization Algorithm for Generative Topographic Mappings
The S-Map is a network with a simple learning algorithm that combines the self-organization capability of the Self-Organizing Map (SOM) and the probabilistic interpretability of the Generative Topographic Mapping (GTM). The simulations suggest that the SMap algorithm has a stronger tendency to self-organize from random initial configuration than the GTM. The S-Map algorithm can be further simpl...
متن کاملMode estimation with topographic maps
The paper reviews thoroughly a variety of issues related to mode estimation. The potential of self-organizing maps as an approach to mode detection is inquired here. The batch version of the standard SOM and a convex adjustment of it are compared with two kernel-based learning rules, namely, the generative topographic mapping and the kernelbased maximum entropy learning rule. A strategy for mod...
متن کاملPruning Rule for kMER-Based Acquisition of the Global Topographic Feature Map
For a kernel-based topographic map formation, kMER (kernel-based maximum entropy learning rule) was proposed by Van Hulle, and some effective learning rules related to kMER have been proposed so far with many applications. However, no discusions have been made concerning the determination of the number of units in kMER. This letter describes a unit-pruning rule, which permits automatic contruct...
متن کاملDevelopmental pruning of synapses and category learning
After an initial peak, the number of synapses in mammalian cerebral cortex decreases in the formative period and throughout adult life. However, if synapses are taken to reflect circuit complexity, the issue arises of how to reconcile pruning with the increasing complexity of the representations acquired in successive stages of development. Taking these two conflicting requirements as an archit...
متن کاملA Uni ed Neural Network Model for the Self-organization of Topographic Receptive Fields and Lateral Interaction
A self-organizing neural network model for the simultaneous development of topographic receptive elds and lateral interactions in cortical maps is presented. Both aaerent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. AAerent input weights of each neuron self-organize into hill-shaped prooles, receptive elds organize topographica...
متن کامل