Distributed Representation Scheme Research Articles

Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB

The current paper reviews a connectionist model, the recurrent neural network with parametric biases (RNNPB), in which multiple behavior schemata can be learned by the network in a distributed manner. The parametric biases in the network play an essential role in both generating and recognizing behavior patterns. They act as a mirror system by means of self-organizing adequate memory structures. Three different robot experiments are reviewed: robot and user interactions; learning and generating different types of dynamic patterns; and linguistic-behavior binding. The hallmark of this study is explaining how self-organizing internal structures can contribute to generalization in learning, and diversity in behavior generation, in the proposed distributed representation scheme.

Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment

This paper investigates how behavior primitives are self-organized in a neural network model utilizing a distributed representation scheme. The model is characterized by so-called parametric biases which adaptively modulate the encoding of different behavior patterns in a single recurrent neural net (RNN). Our experiments, using a real robot arm, showed that a set of end-point and oscillatory behavior patterns are learned by self-organizing fixed points and limit cycle dynamics that form behavior primitives. It was also found that diverse novel behavior patterns can be generated by modulating the parametric biases arbitrarily. Our analysis showed that such diversity in behavior generation emerges because a nonlinear map is self-organized between the space of parametric biases and that of the behavior patterns. The origin of the observed nonlinearity from the distributed representation is discussed. This paper investigates how behavior primitives are self-organized in a neural network model utilizing a distributed representation scheme. Our robot experiments showed that a set of end-point and oscillatory behavior patterns are learned by self-organizing fixed points and limit cycle dynamics that form behavior primitives. It was also found that diverse novel behavior patterns, in addition to previously learned patterns, can be generated by taking advantage of nonlinear effects that emerge from the distributed representation.

Integrating structure and meaning: a distributed model of analogical mapping

In this paper we present Drama, a distributed model of analogical mapping that integrates semantic and structural constraints on constructing analogies. Specifically, Drama uses holographic reduced representations (Plate, 1994), a distributed representation scheme, to model the effects of structure and meaning on human performance of analogical mapping. Drama is compared to three symbolic models of analogy (SME, Copycat, and ACME) and one partially distributed model (LISA). We describe Drama’s performance on a number of example analogies and assess the model in terms of neurological and psychological plausibility. We argue that Drama’s successes are due largely to integrating structural and semantic constraints throughout the mapping process. We also claim that Drama is an existence proof of using distributed representations to model high-level cognitive phenomena.

Integrating structure and meaning: a distributed model of analogical mapping

AbstractIn this paper we present Drama, a distributed model of analogical mapping that integrates semantic and structural constraints on constructing analogies. Specifically, Drama uses holographic reduced representations (Plate, 1994), a distributed representation scheme, to model the effects of structure and meaning on human performance of analogical mapping. Drama is compared to three symbolic models of analogy (SME, Copycat, and ACME) and one partially distributed model (LISA). We describe Drama's performance on a number of example analogies and assess the model in terms of neurological and psychological plausibility. We argue that Drama's successes are due largely to integrating structural and semantic constraints throughout the mapping process. We also claim that Drama is an existence proof of using distributed representations to model high‐level cognitive phenomena.

On encoding neural networks to estimate the atmospheric point spread function in a parallel environment

Images of the Earth's surface acquired by high-altitude aircraft or satellites are degraded by the intervening atmosphere. The imaging instrument records not only the signal of the targeted viewing area but also the radiance scattered into the field of view in the near by area. This effect can be characterized by an atmospheric point spread function (PSF). There are many parameters that may affect the PSF. To restore noisy-blurred images, one must understand which parameters influence the PSF and to what degree. This is very important for scientific applications that seek to extract information about environmental systems. In this paper, a design and implementation of a distributed representation scheme and neural networks are presented in order to estimate the atmospheric PSF. The representation scheme exemplifies the conjunctive coding and coarse coding techniques. Neural networks trained using such an appropriately structured representation generate a desired approximation of the PSF with satisfactory processing time. The system is implemented on a parallel system formed of personal computers and workstations. The maximum speed-up is achieved.