Localist Representation Research Articles

Human and Computation-based Music Representation for Gamelan Music

A public database containing representative data of karawitan traditional music is needed as a resource for researchers who study computer music and karawitan. To establish this database, a text-based pitch model for music representation that is both human and computer-based was first investigated. A new model of musical representation that can be read by humans and computers is proposed to support music and computer research on karawitan also known as gamelan music. The model is expected to serve as the initial effort to establish a public database of karawitan music representation data. The proposed model was inspired by Helmholtz Notation and Scientific Pitch Notation and well-established, text-based pitch representation systems. The model was developed not only for pitch number, high or low or middle pitch information (octave information), but for musical elements found in gamelan sheet music pieces that include pitch value and legato signs. The model was named Gendhing Scientific Pitch Notation (GSPN). Ghending is a Javanese word that means “song”. The GSPN model was designed to represent music by formulating musical elements from a sheet music piece. Furthermore, the model can automatically be converted to other music representation formats. In the experiment, data in the GSPN format was implemented to automatically convert sheet music to a binary code with localist representation technique.

Approximating Bayesian inference with a sparse distributed memory system

Approximating Bayesian inference with a sparse distributed memory system

Familiarization: A theory of repetition suppression predicts interference between overlapping cortical representations.

Repetition suppression refers to a reduction in the cortical response to a novel stimulus that results from repeated presentation of the stimulus. We demonstrate repetition suppression in a well established computational model of cortical plasticity, according to which the relative strengths of lateral inhibitory interactions are modified by Hebbian learning. We present the model as an extension to the traditional account of repetition suppression offered by sharpening theory, which emphasises the contribution of afferent plasticity, by instead attributing the effect primarily to plasticity of intra-cortical circuitry. In support, repetition suppression is shown to emerge in simulations with plasticity enabled only in intra-cortical connections. We show in simulation how an extended ‘inhibitory sharpening theory’ can explain the disruption of repetition suppression reported in studies that include an intermediate phase of exposure to additional novel stimuli composed of features similar to those of the original stimulus. The model suggests a re-interpretation of repetition suppression as a manifestation of the process by which an initially distributed representation of a novel object becomes a more localist representation. Thus, inhibitory sharpening may constitute a more general process by which representation emerges from cortical re-organisation.

The Theory of Localist Representation and of a Purely Abstract Cognitive System: The Evidence from Cortical Columns, Category Cells, and Multisensory Neurons.

The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings – in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit. The paper reviews the fundamental discoveries about the columnar organization and finds that it reveals a massively parallel search mechanism. This columnar organization could be the most extensive neurophysiological evidence for the widespread use of localist representation in the brain. The paper also reviews studies of category-selective cells. The evidence for category-selective cells reveals that localist representation is also used to encode complex abstract concepts at the highest levels of processing in the brain. A third major issue is the nature of the cognitive system in the brain and whether there is a form that is purely abstract and encoded by single cells. To provide evidence for a single-cell based purely abstract cognitive system, the paper reviews some of the findings related to multisensory cells. It appears that there is widespread usage of multisensory cells in the brain in the same areas where sensory processing takes place. Plus there is evidence for abstract modality invariant cells at higher levels of cortical processing. Overall, that reveals the existence of a purely abstract cognitive system in the brain. The paper also argues that since there is no evidence for dense distributed representation and since sparse representation is actually used to encode memories, there is actually no evidence for distributed representation in the brain. Overall, it appears that, at an abstract level, the brain is a massively parallel, distributed computing system that is symbolic. The paper also explains how grounded cognition and other theories of the brain are fully compatible with localist representation and a purely abstract cognitive system.

Do arbitrary input–output mappings in parallel distributed processing networks require localist coding?

ABSTRACTThe Parallel Distributed Processing (PDP) approach to cognitive modelling assumes that knowledge is distributed across multiple processing units. This view is typically justified on the basis of the computational advantages and biological plausibility of distributed representations. However, both these assumptions have been challenged. First, there is growing evidence that some neurons respond to information in a highly selective manner. Second, it has been demonstrated that localist representations are better suited for certain computational tasks. In this paper, we continue this line of research by investigating whether localist representations are learned in tasks involving arbitrary input–output mappings. The results imply that the pressure to learn local codes in such tasks is weak, but still there are conditions under which feed-forward PDP networks learn localist representation. Our findings further challenge the assumption that PDP modelling always goes hand in hand with distributed representations and provide directions for future research.

On Findings of Category and Other Concept Cells in the Brain: Some Theoretical Perspectives on Mental Representation

There is substantial neurophysiological evidence from decades of single-cell studies that category and other concept cells exist in both human and animal brains. This indicates that the brain can generalize and create abstract concepts and encode and represent such abstractions using single cells. These single-cell findings cannot be accounted for and explained by the theory of distributed representation and population coding, the dominant theory in the brain sciences. In light of these findings, this paper reexamines the two contending mental representational schemes, localist and distributed, on the basis of computational efficiency, the ability to simultaneously process and activate many different concepts, and the structure for semantic cognition. The evidence for category and concept cells favors localist representation in the brain.

An extension of the localist representation theory: grandmother cells are also widely used in the brain

ON GRANDMOTHER CELLS Based on considerable neurophysiological evidence, Roy (2012) proposed the theory that localist representation is widely used in the brain, starting from the lowest levels of processing. Grandmother cells are a special case of localist representation. In this article, I present the theory that grandmother cells are also widely used in the brain. To support the proposed theory, I present neurophysiological evidence and an analysis of the concept of grandmother cells. Konorski (1967) first predicted the existence of grandmother cells (he called them “gnostic” neurons)— single neurons that respond to complex stimuli such as faces, hands, expressions, objects, and so on. The term “grandmother cell” was introduced by Jerry Lettvin in 1969 (Barlow, 1995). The notion of grandmother cells is very controversial in neuro and cognitive sciences. Barlow (2009, p. 320) claims that grandmother cells exist and “can now be recorded from and studied reliably.” Bowers (2009) has also claimed that the brain uses grandmother cells to code for objects and concepts. However, Plaut and McClelland (2010) and Quian Quiroga et al. (2008; Quian Quiroga and Kreiman, 2010) have vigorously opposed the notion of grandmother cells in the brain.

A theory of the brain: localist representation is used widely in the brain

ON LOCALIST AND DISTRIBUTED REPRESENTATIONS In this article, I present the theory that localist representation is used widely in the brain starting from its earliest levels of processing. Page (2000) argued for localist representation and Bowers (2009) claimed that the brain uses grandmother cells to code for objects and concepts. However, neither Page (2000) nor Bowers (2009) claimed widespread use of localist representation in the brain. So this is a stronger position than that taken by either. To support the proposed theory, I present neurophysiological evidence, both old and new, and an analysis of localist and distributed representation definitions and models. “Meaning and interpretation” on a stand-alone basis is the fundamental character of a localist unit. In arguing for the proposed theory, I bring to the forefront the “meaning and interpretation” aspect of localist cells and the evidence for it in the brain. I also show that localist and distributed models are not different structurally. In fact, any kind of model can be built with localist units. However, localist representation has no claim on the resulting properties of such models or what they can do.

The local is running on the express track: Localist models better facilitate understanding of nervous system function

Artificial neural networks have weaknesses as models of cognition. A conventional neural network has limitations of computational power. The localist representation is at least equal to its competition. We contend that locally connected neural networks are perfectly capable of storing and retrieving the individual features, but the process of reconstruction must be otherwise explained. We support the localist position but propose a “hybrid” model that can begin to explain cognition in anatomically plausible terms.

Modeling knowledge-based inferences in story comprehension

A computational model of inference during story comprehension is presented, in which story situations are represented distributively as points in a high-dimensional “situation-state space.” This state space organizes itself on the basis of a constructed microworld description. From the same description, causal/temporal world knowledge is extracted. The distributed representation of story situations is more flexible than Golden and Rumelhart’s [Discourse Proc 16 (1993) 203] localist representation. A story taking place in the microworld corresponds to a trajectory through situation-state space. During the inference process, world knowledge is applied to the story trajectory. This results in an adjusted trajectory, reflecting the inference of propositions that are likely to be the case. Although inferences do not result from a search for coherence, they do cause story coherence to increase. The results of simulations correspond to empirical data concerning inference, reading time, and depth of processing. An extension of the model for simulating story retention shows how coherence is preserved during retention without controlling the retention process. Simulation results correspond to empirical data concerning story recall and intrusion.

Computation, reduction, and teleology of consciousness

This paper aims to explore mechanistic and teleological explanations of consciousness. In terms of mechanistic explanations, it critiques various existing views, especially those embodied by existing computational cognitive models. In this regard, the paper argues in favor of the explanation based on the distinction between localist (symbolic) representation and distributed representation (as formulated in the connectionist literature), which reduces the phenomenological difference to a mechanistic difference. Furthermore, to establish a teleological explanation of consciousness, the paper discusses the issue of the functional role of consciousness on the basis of the aforementioned mechanistic explanation. A proposal based on synergistic interaction between the conscious and the unconscious is advanced that encompasses various existing views concerning the functional role of consciousness. This two-step deepening explanation has some empirical support, in the form of a cognitive model and various cognitive data that it captures.

Localist representation can improve efficiency for detection and counting

Almost all representations have both distributed and localist aspects, depending upon what properties of the data are being considered. With noisy data, features represented in a localist way can be detected very efficiently, and in binary representations they can be counted more efficiently than those represented in a distributed way. Brains operate in noisy environments, so the localist representation of behaviourally important events is advantageous, and fits what has been found experimentally. Distributed representations require more neurons to perform as efficiently, but they do have greater versatility.

Accounting for the Computational Basis of Consciousness: A Connectionist Approach

This paper argues for an explanation of the mechanistic (computational) basis of consciousness that is based on the distinction between localist (symbolic) representation and distributed representation, the ideas of which have been put forth in the connectionist literature. A model is developed to substantiate and test this approach. The paper also explores the issue of the functional roles of consciousness, in relation to the proposed mechanistic explanation of consciousness. The model, embodying the representational difference, is able to account for the functional role of consciousness, in the form of the synergy between the conscious and the unconscious. The fit between the model and various cognitive phenomena and data (documented in the psychological literatures) is discussed to accentuate the plausibility of the model and its explanation of consciousness. Comparisons with existing models of consciousness are made in the end.