On High-Level Inferencing and the Variable Binding Problem in Connectionist Networks

1990 ◽  
pp. 180-185 ◽  
Author(s):  
Steffen Hölldobler
Keyword(s):  
Binding Problem ◽  
Variable Binding ◽  
Connectionist Networks ◽  
High Level

scholarly journals A (Somewhat) New Solution to the Variable Binding Problem

2008 ◽  
Vol 20 (9) ◽  
pp. 2361-2378 ◽  
Author(s):  
Leon Barrett ◽  
Jerome Feldman ◽  
Liam Mac Dermed
Keyword(s):  
Human Brain ◽  
Computer System ◽  
System Modeling ◽  
Binding Problem ◽  
Temporal Binding ◽  
Variable Binding ◽  
Temporal Synchrony ◽  
Human Ability ◽  
Binding Models ◽  
Short Signatures

To perform automatic, unconscious inference, the human brain must solve the binding problem by correctly grouping properties with objects. Temporal binding models like SHRUTI already suggest much of how this might be done in a connectionist and localist way by using temporal synchrony. We propose a set of alternatives to temporal synchrony mechanisms that instead use short signatures. This serves two functions: it allows us to explore an additional biologically plausible alternative, and it allows us to extend and improve the capabilities of these models. These extensions model the human ability to both perform unification and handle multiple instantiations of logical terms. To verify our model's feasibility, we simulate it with a computer system modeling simple, neuron-like computations.

scholarly journals THERMAL-AWARE HIGH-LEVEL SYNTHESIS BASED ON NETWORK FLOW METHOD

2009 ◽  
Vol 18 (05) ◽  
pp. 965-984 ◽  
Author(s):  
PILOK LIM ◽  
KI-SEOK CHUNG ◽  
TAEWHAN KIM
Keyword(s):  
Network Flow ◽  
Functional Module ◽  
Primary Objective ◽  
Peak Temperature ◽  
Heat Diffusion ◽  
High Level Synthesis ◽  
Binding Problem ◽  
Flow Method ◽  
Chip Temperature ◽  
High Level

Controlling the chip temperature is becoming one of the important design considerations, since temperature adversely and seriously affects many of design qualities, such as reliability, performance and power of chip, and increases the packaging cost. In this work, we address a new problem of thermal-aware functional module binding in high-level synthesis, in which the objective is to minimize the peak temperature of the chip. Two key contributions are (1) to solve the binding problem with the primary objective of minimizing the "peak" switched capacitance of modules and the secondary objective of minimizing the "total" switched capacitance of modules and (2) to control the switched capacitances with respect to the floorplan of modules in a way to minimize the "peak" heat diffusion between modules. For (1), our proposed thermal-aware binding algorithm, called TA-b, formulates the thermal-aware binding problem into a problem of repeated utilization of network flow method, and solve it effectively. For (2), TA-b is extended, called TA-bf, to take into account a given floorplan information of functional modules, if exists, of modules to be practically effective. Through experiments using a set of benchmarks, it is shown that TA-bf is able to use 10.1°C and 11.8°C lower peak temperature on the average, compared to that of the conventional low-power and thermal-aware methods, which target to minimizing total switched capacitance only in Ref. 20 and to minimizing peak switched capacitance only in Ref. 16, respectively. Additionally, we confirm, from the experiments, that the reduced peak temperature saves leakage power significantly, implying that controlling chip temperature is critically important for reducing leakage current as well.

Instruction and High-level Learning in Connectionist Networks

1989 ◽  
Vol 1 (2) ◽  
pp. 161-180 ◽  
Author(s):  
JOACHIM DIEDERICH
Keyword(s):  
Connectionist Networks ◽  
High Level

Scheduling and variable binding for improved testability in high level synthesis

1998 ◽  
Vol 24 (6) ◽  
pp. 441-461
Author(s):  
A.A. Ismaeel ◽  
R. Mathew ◽  
R. Bhatnagar
Keyword(s):  
High Level Synthesis ◽  
Variable Binding ◽  
High Level

On Variable Binding in Connectionist Networks

1992 ◽  
Vol 4 (2) ◽  
pp. 93-124 ◽  
Author(s):  
RON SUN
Keyword(s):  
Variable Binding ◽  
Connectionist Networks

Rules and Variables in Neural Nets

1991 ◽  
Vol 3 (1) ◽  
pp. 121-134 ◽  
Author(s):  
Venkat Ajjanagadde ◽  
Lokendra Shastri
Keyword(s):  
Knowledge Representation ◽  
Fundamental Problem ◽  
Neural Nets ◽  
Knowledge Representation And Reasoning ◽  
Binding Problem ◽  
Variable Binding ◽  
Reasoning System ◽  
Synchronous Firing ◽  
Abstract Knowledge ◽  
Dynamic Structures

A fundamental problem that must be addressed by connectionism is that of creating and representing dynamic structures (Feldman 1982; von der Malsburg 1985). In the context of reasoning with systematic and abstract knowledge, this problem takes the form of the variable binding problem. We describe a biologically plausible solution to this problem and outline how a knowledge representation and reasoning system can use this solution to perform a class of predictive inferences with extreme efficiency. The proposed system solves the variable binding problem by propagating rhythmic patterns of activity wherein dynamic bindings are represented as the synchronous firing of appropriate nodes.

Parallel reasoning in structured connectionist networks: Signatures versus temporal synchrony

1996 ◽  
Vol 19 (2) ◽  
pp. 328-331 ◽  
Author(s):  
Trent E. Lange ◽  
Michael G. Dyer
Keyword(s):  
Difficult Problem ◽  
Binding Problem ◽  
Dynamic Binding ◽  
Language Understanding ◽  
Temporal Synchrony ◽  
Connectionist Networks ◽  
Synchronous Firing ◽  
Abstract Knowledge ◽  
Knowledge Rules

Shastri & Ajjanagadde (1993) (S&A) argue convincingly that both structured connectionist networks and parallel dynamic inferencing are necessary for reflexive reasoning - a kind of inferencing and reasoning that occurs rapidly, spontaneously, and without conscious effort, and which seems necessary for everyday tasks such as natural language understanding. As S&A describe, reflexive reasoning requires a solution to thedynamic binding problem, that is, how to encode systematic and abstract knowledge and instantiate it in specific situations to draw appropriate inferences. Although symbolic artificial intelligence systems trivially solve the dynamic binding problem using computers' registers and pointers, it has remained a difficult problem for connectionist systems (see Fodor & Pylyshyn 1988). S&A's temporal synchrony solution to the dynamic binding problem using synchronous firing of argument units and the entities that are bound to them illustrates one way in which connectionist networks can do thisusing a constrained but important class of long-term knowledge rules. Their structured connectionist solution allows dynamic inferencing to proceed in parallel and therefore has a number of advantages for reflexive reasoning over most other connectionist and symbolic systems.

Transcription factor/DNA interactions visualized by electron spectroscopic imaging

1992 ◽  
Vol 50 (1) ◽  
pp. 450-451
Author(s):  
David P. Bazett-Jones ◽  
Mark L. Brown
Keyword(s):  
Transcription Factor ◽  
Dna Sequences ◽  
Transcription Initiation ◽  
Molecular Mechanisms ◽  
Phosphorus Content ◽  
Spectroscopic Imaging ◽  
Electron Spectroscopic Imaging ◽  
Dna Backbone ◽  
Two Parameters ◽  
High Level

A multisubunit RNA polymerase enzyme is ultimately responsible for transcription initiation and elongation of RNA, but recognition of the proper start site by the enzyme is regulated by general, temporal and gene-specific trans-factors interacting at promoter and enhancer DNA sequences. To understand the molecular mechanisms which precisely regulate the transcription initiation event, it is crucial to elucidate the structure of the transcription factor/DNA complexes involved. Electron spectroscopic imaging (ESI) provides the opportunity to visualize individual DNA molecules. Enhancement of DNA contrast with ESI is accomplished by imaging with electrons that have interacted with inner shell electrons of phosphorus in the DNA backbone. Phosphorus detection at this intermediately high level of resolution (≈lnm) permits selective imaging of the DNA, to determine whether the protein factors compact, bend or wrap the DNA. Simultaneously, mass analysis and phosphorus content can be measured quantitatively, using adjacent DNA or tobacco mosaic virus (TMV) as mass and phosphorus standards. These two parameters provide stoichiometric information relating the ratios of protein:DNA content.

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