Biomedical subjects

R Miikkulainen

Publications and source records attributed to R Miikkulainen.

5 recordsLinked to original sources

PubMed2001

Constrained emergence of universals and variation in syllable systems.

A computational model of emergent syllable systems is developed based on a set of functional constraints on syllable systems and the assumption that language structure emerges through cumulative change over time. The constraints were derived from general communicative factors as well as from the phonetic principles of perceptual distinctiveness and articulatory ease. Through evolutionary optimization, the model generated mock vocabularies optimized for the given constraints. Several simulations were run to understand how these constraints might define the emergence of universals and variation in complex sound systems. The predictions were that (1) CV syllables would be highly frequent in all vocabularies evolved under the constraints; (2) syllables with consonant clusters, consonant codas, and vowel onsets would occur much less frequently; (3) a relationship would exist between the number of syllable types in a vocabulary and the average word length in the vocabulary; (4) different syllable types would emerge according to, what we termed, an iterative principle of syllable structure and their frequency would be directly related to their complexity; and (5) categorical differences would emerge between vocabularies evolved under the same constraints. Simulation results confirmed these predictions and provided novel insights into why regularities and differences may occur across languages. Specifically, the model suggested that both language universals and variation are consistent with a set of functional constraints that are fixed relative to one another. Language universals reflect underlying constraints on the system and language variation represents the many different and equally-good solutions to the unique problem defined by these constraints.

Computer Simulation↗

PubMed2000

Tilt aftereffects in a self-organizing model of the primary visual cortex.

RF-LISSOM, a self-organizing model of laterally connected orientation maps in the primary visual cortex, was used to study the psychological phenomenon known as the tilt aftereffect. The same self-organizing processes that are responsible for the long-term development of the map are shown to result in tilt aftereffects over short timescales in the adult. The model permits simultaneous observation of large numbers of neurons and connections, making it possible to relate high-level phenomena to low-level events, which is difficult to do experimentally. The results give detailed computational support for the long-standing conjecture that the direct tilt aftereffect arises from adaptive lateral interactions between feature detectors. They also make a new prediction that the indirect effect results from the normalization of synaptic efficacies during this process. The model thus provides a unified computational explanation of self-organization and both the direct and indirect tilt aftereffect in the primary visual cortex.

Adaptation, Ocular↗

PubMed1997

Topographic receptive fields and patterned lateral interaction in a self-organizing model of the primary visual cortex.

This article presents a self-organizing neural network model for the simultaneous and cooperative development of topographic receptive fields and lateral interactions in cortical maps. Both afferent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. Afferent input weights of each neuron self-organize into hill-shaped profiles, receptive fields organize topographically across the network, and unique lateral interaction profiles develop for each neuron. The model demonstrates how patterned lateral connections developed based on correlated activity and explains why lateral connection patterns closely follow receptive field properties such as ocular dominance.

Brain Mapping↗

PubMed1997

Forming neural networks through efficient and adaptive coevolution

This article demonstrates the advantages of a cooperative, coevolutionary search in difficult control problems. The symbiotic adaptive neuroevolution (SANE) system coevolves a population of neurons that cooperate to form a functioning neural network. In this process, neurons assume different but overlapping roles, resulting in a robust encoding of control behavior. SANE is shown to be more efficient and more adaptive and to maintain higher levels of diversity than the more common network-based population approaches. Further empirical studies illustrate the emergent neuron specializations and the different roles the neurons assume in the population.

Journal Article↗

PubMed1992

Trace feature map: a model of episodic associative memory.

An approach to episodic associative memory is presented, which has several desirable properties as a human memory model. The design is based on topological feature map representation of data. An ordinary feature map is a classifier, mapping an input vector onto a topologically meaningful location on the map. A trace feature map, in addition, creates a memory trace on that location. The traces can be stored episodically in a single presentation, and retrieved with a partial cue. Nearby traces overlap, which results in plausible memory interference behavior. Performance degrades gracefully as the memory is overloaded. More recent traces are easier to recall as are traces that are unique in the memory.

Association Learning↗