Intraframe Coding with Massive Dictionaries of Tree-Structured Representations

2014 ◽  
Author(s):  
Hong-Cyuan Wang ◽  
Yung-Yao Chen ◽  
Wen-Huang Cheng ◽  
Shih-Wei Sun ◽  
Kai-Lung Hua
Keyword(s):  
Structured Representations ◽  
Intraframe Coding

The Role of Neuronal Synchronization in Response Selection: A Biologically Plausible Theory of Structured Representations in the Visual Cortex

1996 ◽  
Vol 8 (6) ◽  
pp. 603-625 ◽  
Author(s):  
Pieter R. Roelfsema ◽  
Andreas K. Engel ◽  
Peter König ◽  
Wolf Singer
Keyword(s):  
Visual Cortex ◽  
Response Selection ◽  
Sensory Information ◽  
Motor Program ◽  
Dynamic Parameter ◽  
Object Identification ◽  
Multiple Objects ◽  
Structured Representations ◽  
Purposeful Behavior ◽  
Behavioral Requirements

Recent experimental results in the visual cortex of cats and monkeys have suggested an important role for synchronization of neuronal activity on a millisecond time scale. Synchronization has been found to occur selectively between neuronal responses to related image components. This suggests that not only the firing rates of neurons but also the relative timing of their action potentials is used as a coding dimension. Thus, a powerful relational code would be available, in addition to the rate code, for the representation of perceptual objects. This could alleviate difficulties in the simultaneous representation of multiple objects. In this article we present a set of theoretical arguments and predictions concerning the mechanisms that could group neurons responding to related image components into coherently active aggregates. Synchrony is likely to be mediated by synchronizing connections; we introduce the concept of an interaction skeleton to refer to the subset of synchronizing connections that are rendered effective by a particular stimulus configuration. If the image is segmented into objects, these objects can typically be segmented further into their constituent parts. The synchronization behavior of neurons that represent the various image components may accurately reflect this hierarchical clustering. We propose that the range of synchronizing interactions is a dynamic parameter of the cortical network, so that the grain of the resultant grouping process may be adapted to the actual behavioral requirements. It can be argued that different aspects of purposeful behavior rely on separable processes by which sensory input is transformed into adjustments of motor activity. Indeed, neurophysiological evidence has suggested separate processing streams originating in the primary visual cortex for object identification and sensorimotor coordination. However, such a separation calls for a mechanism that avoids interference effects in the presence of multiple objects, or when multiple motor programs are simultaneously prepared. In this article we suggest that synchronization between responses of neurons in both the visual cortex and in areas that are involved in response selection and execution might allow for a selective routing of sensory information to the appropriate motor program.

scholarly journals A NEW LINEAR GENETIC PROGRAMMING APPROACH BASED ON STRAIGHT LINE PROGRAMS: SOME THEORETICAL AND EXPERIMENTAL ASPECTS

2009 ◽  
Vol 18 (05) ◽  
pp. 757-781 ◽  
Author(s):  
CÉSAR L. ALONSO ◽  
JOSÉ LUIS MONTAÑA ◽  
JORGE PUENTE ◽  
CRUZ ENRIQUE BORGES
Keyword(s):  
Data Structure ◽  
Genetic Programming ◽  
Computer Programs ◽  
Symbolic Regression ◽  
Programming Approach ◽  
Linear Genetic Programming ◽  
Straight Line ◽  
Structured Representations ◽  
Regression Problems ◽  
Straight Line Programs

Tree encodings of programs are well known for their representative power and are used very often in Genetic Programming. In this paper we experiment with a new data structure, named straight line program (slp), to represent computer programs. The main features of this structure are described, new recombination operators for GP related to slp's are introduced and a study of the Vapnik-Chervonenkis dimension of families of slp's is done. Experiments have been performed on symbolic regression problems. Results are encouraging and suggest that the GP approach based on slp's consistently outperforms conventional GP based on tree structured representations.

Exploring Cognition with Brain–Machine Interfaces

2022 ◽  
Vol 73 (1) ◽  
pp. 131-158
Author(s):  
Richard A. Andersen ◽  
Tyson Aflalo ◽  
Luke Bashford ◽  
David Bjånes ◽  
Spencer Kellis
Keyword(s):  
Posterior Parietal Cortex ◽  
Sensory Information ◽  
Movement Control ◽  
Brain Machine Interfaces ◽  
Motor Commands ◽  
Cortical Motor ◽  
Structured Representations ◽  
Posterior Parietal ◽  
Integration Planning ◽  
Motor Actions

Traditional brain–machine interfaces decode cortical motor commands to control external devices. These commands are the product of higher-level cognitive processes, occurring across a network of brain areas, that integrate sensory information, plan upcoming motor actions, and monitor ongoing movements. We review cognitive signals recently discovered in the human posterior parietal cortex during neuroprosthetic clinical trials. These signals are consistent with small regions of cortex having a diverse role in cognitive aspects of movement control and body monitoring, including sensorimotor integration, planning, trajectory representation, somatosensation, action semantics, learning, and decision making. These variables are encoded within the same population of cells using structured representations that bind related sensory and motor variables, an architecture termed partially mixed selectivity. Diverse cognitive signals provide complementary information to traditional motor commands to enable more natural and intuitive control of external devices.

Structural Alignment in Comparison: No Difference Without Similarity

1994 ◽  
Vol 5 (3) ◽  
pp. 152-158 ◽  
Author(s):  
Dedre Gentner ◽  
Arthur B. Markman
Keyword(s):  
Cognitive Processes ◽  
Potential Role ◽  
Structural Alignment ◽  
Comparison Process ◽  
Structured Representations ◽  
Single Difference

Theories of similarity generally agree that the similarity of a pair increases with its commonalities and decreases with its differences. Recent research suggests that this comparison process involves an alignment of structured representations yielding commonalities, differences related to the commonalities, and differences unrelated to the commonalities. One counterintuitive prediction of this view is that it should be easier to find the differences between pairs of similar items than to find the differences between pairs of dissimilar items. This prediction is particularly strong for differences that are related to the commonalities. We tested this prediction in two experiments in which subjects listed a single difference for each of a number of word pairs. The results are consistent with the predictions of structural alignment. In light of these findings, we discuss the potential role of structural alignment in other cognitive processes that involve comparisons.

scholarly journals Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses

2019 ◽  
Vol 375 (1791) ◽  
pp. 20190304 ◽  
Author(s):  
Ryan Calmus ◽  
Benjamin Wilson ◽  
Yukiko Kikuchi ◽  
Christopher I. Petkov
Keyword(s):  
Temporal Cortex ◽  
Brain Regions ◽  
Position Information ◽  
Sequence Processing ◽  
Oscillatory Dynamics ◽  
Structured Representations ◽  
Dorsolateral Prefrontal ◽  
Frontal Operculum ◽  
Structured Information ◽  
The Brain

Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms for segmenting continuous streams of sensory input and establishing representations of dependencies remain largely unknown, as do the transformations and computations occurring between the brain regions involved in these aspects of sequence processing. We propose a blueprint for a neurobiologically informed and informing computational model of sequence processing (entitled: Vector-symbolic Sequencing of Binding INstantiating Dependencies, or VS-BIND). This model is designed to support the transformation of serially ordered elements in sensory sequences into structured representations of bound dependencies, readily operates on multiple timescales, and encodes or decodes sequences with respect to chunked items wherever dependencies occur in time. The model integrates established vector symbolic additive and conjunctive binding operators with neurobiologically plausible oscillatory dynamics, and is compatible with modern spiking neural network simulation methods. We show that the model is capable of simulating previous findings from structured sequence processing tasks that engage fronto-temporal regions, specifying mechanistic roles for regions such as prefrontal areas 44/45 and the frontal operculum during interactions with sensory representations in temporal cortex. Finally, we are able to make predictions based on the configuration of the model alone that underscore the importance of serial position information, which requires input from time-sensitive cells, known to reside in the hippocampus and dorsolateral prefrontal cortex. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.

scholarly journals Towards Converting Clinical Phrases into SNOMED CT Expressions

2013 ◽  
Vol 6s1 ◽  
pp. BII.S11645 ◽  
Author(s):  
Rohit J. Kate
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
Clinical Applications ◽  
Important Task ◽  
Learning Approach ◽  
Snomed Ct ◽  
Clinical Text ◽  
Structured Representations ◽  
Machine Learning Approach ◽  
Clinical Terminology

Converting information contained in natural language clinical text into computer-amenable structured representations can automate many clinical applications. As a step towards that goal, we present a method which could help in converting novel clinical phrases into new expressions in SNOMED CT, a standard clinical terminology. Since expressions in SNOMED CT are written in terms of their relations with other SNOMED CT concepts, we formulate the important task of identifying relations between clinical phrases and SNOMED CT concepts. We present a machine learning approach for this task and using the dataset of existing SNOMED CT relations we show that it performs well.

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