Biomedical subjects

Risto Miikkulainen

Publications and source records attributed to Risto Miikkulainen.

7 recordsLinked to original sources

PubMed2005

Associating unseen events: semantically mediated formation of episodic associations.

In prior work, we developed a computational model of how episodic associations between words are formed. Simulating associative learning, the model indicated that strongly associated semantically unrelated words facilitate the episodic association of other exemplars included in their semantic neighborhoods. This prediction was supported empirically by the present study. First, the incidental formation of strong associations between unrelated words, such as dog and table, improved cued recall of weak associations formed incidentally between semantic neighbors, like cat and chair. Second, deciding that two words were semantically unrelated was facilitated by forming strong associations between other words in their respective semantic neighborhoods, even if the tested pair was not presented at study. Together with the computational model, the present results demonstrate that forming episodic associations between words can implicitly mediate the association of other exemplars from the same semantic categories and reveal a mechanism by which the semantic system contributes to the formation of new episodic associations.

Adolescent↗

PubMed2004

Contour integration and segmentation with self-organized lateral connections.

Contour integration in low-level vision is believed to occur based on lateral interaction between neurons with similar orientation tuning. How such interactions could arise in the brain has been an open question. Our model suggests that the interactions can be learned through input-driven self-organization, i.e., through the same mechanism that underlies many other developmental and functional phenomena in the visual cortex. The model also shows how synchronized firing mediated by these lateral connections can represent the percept of a contour, resulting in performance similar to that of human contour integration. The model further demonstrates that contour integration performance can differ in different parts of the visual field, depending on what kinds of input distributions they receive during development. The model thus grounds an important perceptual phenomenon onto detailed neural mechanisms so that various structural and functional properties can be measured and predictions can be made to guide future experiments.

Animals↗

PubMed2004

Scaling self-organizing maps to model large cortical networks.

Self-organizing computational models with specific intracortical connections can explain many functional features of visual cortex, such as topographic orientation and ocular dominance maps. However, due to their computational requirements, it is difficult to use such detailed models to study large-scale phenomenal like object segmentation and binding, object recognition, tilt illusions, optic flow, and fovea-periphery differences. This article introduces two techniques that make large simulations practical. First, we show how parameter scaling equations can be derived for laterally connected self-organizing models. These equations result in quantitatively equivalent maps over a wide range of simulation sizes, making it possible to debug small simulations and then scale them up only when needed. Parameter scaling also allows detailed comparison of biological maps and parameters between individuals and species with different brain region sizes. Second, we use parameter scaling to implement a new growing map method called GLISSOM, which dramatically reduces the memory and computational requirements of large self-organizing networks. With GLISSOM, it should be possible to simulate all of human V1 at the single-column level using current desktop workstations. We are using these techniques to develop a new simulator Topographica, which will help make it practical to perform detailed studies of large-scale phenomena in topographic maps.

Animals↗

PubMed2003

Learning innate face preferences.

Newborn humans preferentially orient to facelike patterns at birth, but months of experience with faces are required for full face processing abilities to develop. Several models have been proposed for how the interaction of genetic and environmental influences can explain these data. These models generally assume that the brain areas responsible for newborn orienting responses are not capable of learning and are physically separate from those that later learn from real faces. However, it has been difficult to reconcile these models with recent discoveries of face learning in newborns and young infants. We propose a general mechanism by which genetically specified and environment-driven preferences can coexist in the same visual areas. In particular, newborn face orienting may be the result of prenatal exposure of a learning system to internally generated input patterns, such as those found in PGO waves during REM sleep. Simulating this process with the HLISSOM biological model of the visual system, we demonstrate that the combination of learning and internal patterns is an efficient way to specify and develop circuitry for face perception. This prenatal learning can account for the newborn preferences for schematic and photographic images of faces, providing a computational explanation for how genetic influences interact with experience to construct a complex adaptive system.

Face↗

PubMed2003

A taxonomy for artificial embryogeny.

A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evolutionary mechanisms where the same genes are used multiple times in the process of building a phenotype. Such gene reuse allows compact representations of very complex phenotypes. Development is a natural choice for implementing indirect encodings, if only because nature itself uses this very process. Motivated by the development of embryos in nature, we define artificial embryogeny (AE) as the subdiscipline of evolutionary computation (EC) in which phenotypes undergo a developmental phase. An increasing number of AE systems are currently being developed, and a need has arisen for a principled approach to comparing and contrasting, and ultimately building, such systems. Thus, in this paper, we develop a principled taxonomy for AE. This taxonomy provides a unified context for long-term research in AE, so that implementation decisions can be compared and contrasted along known dimensions in the design space of embryogenic systems. It also allows predicting how the settings of various AE parameters affect the capacity to efficiently evolve complex phenotypes.

Classification↗

PubMed2002

Evolving neural networks through augmenting topologies.

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.

Algorithms↗

PubMed1997

Convergence-Zone Episodic Memory: Analysis and Simulations.

Human episodic memory provides a seemingly unlimited storage for everyday experiences, and a retrieval system that allows us to access the experiences with partial activation of their components. The system is believed to consist of a fast, temporary storage in the hippocampus, and a slow, long-term storage within the neocortex. This paper presents a neural network model of the hippocampal episodic memory inspired by Damasio's idea of Convergence Zones. The model consists of a layer of perceptual feature maps and a binding layer. A perceptual feature pattern is coarse coded in the binding layer, and stored on the weights between layers. A partial activation of the stored features activates the binding pattern, which in turn reactivates the entire stored pattern. For many configurations of the model, a theoretical lower bound for the memory capacity can be derived, and it can be an order of magnitude or higher than the number of all units in the model, and several orders of magnitude higher than the number of binding-layer units. Computational simulations further indicate that the average capacity is an order of magnitude larger than the theoretical lower bound, and making the connectivity between layers sparser causes an even further increase in capacity. Simulations also show that if more descriptive binding patterns are used, the errors tend to be more plausible (patterns are confused with other similar patterns), with a slight cost in capacity. The convergence-zone episodic memory therefore accounts for the immediate storage and associative retrieval capability and large capacity of the hippocampal memory, and shows why the memory encoding areas can be much smaller than the perceptual maps, consist of rather coarse computational units, and are only sparsely connected to the perceptual maps. Copyright 1997 Elsevier Science Ltd.

Journal Article↗