A pointer's hypothesis of general intelligence evolved from domain-specific demands

2017 ◽  
Vol 40 ◽  
Author(s):  
X. T. (Xiao-Tian) Wang
Keyword(s):  
Brain Function ◽  
Higher Order ◽  
Verbal Communication ◽  
General Intelligence ◽  
Order Function ◽  
Task Specificity ◽  
Domain Specific ◽  
Multiple Domain ◽  
Adaptive Problem

AbstractA higher-order function may evolve phylogenetically if it is demanded by multiple domain-specific modules. Task-specificity to solve a unique adaptive problem (e.g., foraging or mating) should be distinguished from function-specificity to deal with a common computational demand (e.g., numeracy, verbal communication) required by many tasks. A localized brain function is likely a result of such common computational demand.

Synthesis of Harmonic Motion Generating Linkages—Part I: Function Generation

1988 ◽  
Vol 110 (1) ◽  
pp. 16-21 ◽  
Author(s):  
K. Farhang ◽  
A. Midha ◽  
A. K. Bajaj
Keyword(s):  
Harmonic Functions ◽  
Higher Order ◽  
Order Function ◽  
Harmonic Motion ◽  
Function Generation ◽  
Synthesis Techniques ◽  
Input Motion

This paper deals with the first and higher-order function-generation problems in the synthesis of linkages with relatively small input cranks. Such linkages tend to produce nearly simple harmonic motions at the output members. Owing to this distinction, the generality of the conventional synthesis techniques is no longer applicable. Thus, in function generation, only harmonic functions of the input motion may be expected to be synthesized for output motions.

Branch-and-bound as a higher-order function

1991 ◽  
Vol 33 (5) ◽  
pp. 379-402 ◽  
Author(s):  
G. P. McKeown ◽  
V. J. Rayward-Smith ◽  
H. J. Turpin
Keyword(s):  
Branch And Bound ◽  
Higher Order ◽  
Order Function

scholarly journals Abnormal Saccadic Intrusions with Alzheimer's Disease in Darkness

2019 ◽  
Vol 16 (4) ◽  
pp. 293-301
Author(s):  
Kiyotaka Nakamagoe ◽  
Shiori Yamada ◽  
Rio Kawakami ◽  
Tadachika Koganezawa ◽  
Akira Tamaoka
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Higher Order ◽  
Control Group ◽  
Order Function ◽  
Cortical Damage ◽  
Significant Difference ◽  
Eyes Open ◽  
Proportional Relationship ◽  
Saccadic Intrusions

Background: Classified as saccadic intrusions, Square-Wave Jerks (SWJs) have been observed during Visual Fixation (VF) in Alzheimer’s Disease (AD). However, the pathological significance of this phenomenon remains unclear. Objective: The present study analyzed the characteristics of SWJs in patients with AD with their eyes open in the dark without VF. Methods: Fifteen patients with AD and 15 healthy age- and sex-matched controls were investigated and compared. Saccadic intrusions with and without VF were detected as SWJs and measured using an electronystagmogram. Results: No significant difference in the frequency of SWJs was observed between control and AD groups with VF, but significantly more SWJs were observed in the AD group than in the control group in the absence of VF (p<0.01). In the control group, the frequency of SWJs was significantly higher with VF as compared to without VF. Conversely, the frequency in the AD group was significantly higher without VF. Furthermore, a directly proportional relationship was observed between the frequency of SWJs and higher-order function (R>0.55) in the AD group. Conclusion: SWJs without VF may have pathological significance in AD. In healthy individuals, SWJs are generated by VF and suppressed without VF. Conversely, in AD, SWJs are generated rather than suppressed in the absence of VF. These pathognomonic SWJs without VF also appear to be correlated with higher-order dysfunction, reflecting AD-related cortical damage. These findings suggest that pathological SWJs without VF observed in AD derive from cortical damage and may constitute an important marker of a higher-order function.

Synthesis of Harmonic Motion Generating Linkages: Part I — Function Generation

1987 ◽  
Author(s):  
K. Farhang ◽  
A. Midha ◽  
A. K. Bajaj
Keyword(s):  
Harmonic Functions ◽  
Higher Order ◽  
Order Function ◽  
Harmonic Motion ◽  
Function Generation ◽  
Synthesis Techniques ◽  
Input Motion

Abstract This paper deals with the first- and higher-order function generation problems in the synthesis of linkages with relatively small input cranks. Such linkages tend to produce nearly simple harmonic motions at the output members. Owing to this distinction, the generality of the conventional synthesis techniques is no longer applicable. Thus, in function generation, only harmonic functions of the input motion may be expected to be synthesized for output motions.

Analysis of Quantization Effects on Higher Order Function and Multilayer Feedforward Neural Networks

2010 ◽  
pp. 187-222 ◽  
Author(s):  
Minghu Jiang ◽  
Georges Gielen ◽  
Lin Wang
Keyword(s):  
Neural Networks ◽  
Fixed Point ◽  
Statistical Models ◽  
Feedforward Neural Networks ◽  
Performance Degradation ◽  
Higher Order ◽  
Floating Point ◽  
Order Function ◽  
Network Layers ◽  
And Performance

In this chpater we investigate the combined effects of quantization and clipping on Higher Order function neural networks (HOFNN) and multilayer feedforward neural networks (MLFNN). Statistical models are used to analyze the effects of quantization in a digital implementation. We analyze the performance degradation caused as a function of the number of fixed-point and floating-point quantization bits under the assumption of different probability distributions for the quantized variables, and then compare the training performance between situations with and without weight clipping, and derive in detail the effect of the quantization error on forward and backward propagation. No matter what distribution the initial weights comply with, the weights distribution will approximate a normal distribution for the training of floating-point or high-precision fixed-point quantization. Only when the number of quantization bits is very low, the weights distribution may cluster to ±1 for the training with fixed-point quantization. We establish and analyze the relationships for a true nonlinear neuron between inputs and outputs bit resolution, training and quantization methods, the number of network layers, network order and performance degradation, all based on statistical models, and for on-chip and off-chip training. Our experimental simulation results verify the presented theoretical analysis.

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