scholarly journals Number of autonomous agents in a neural network

2021 ◽  
Author(s):  
Subhash Kak
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Autonomous Agents ◽  
Size Structure ◽  
Cognitive Capacity ◽  
Scale Invariant ◽  
Cognitive Agents ◽  
Invariant Structure ◽  
Underlying Space ◽  
Speed Of Propagation

This paper considers several aspects of the relationship between size, structure, speed of propagation and the number of autonomous cognitive agents in a neural network. Whereas, memory and function generation capacities of neural networks with scale invariant structure have been investigated extensively, the number of autonomous agents has not received prior attention. We propose the emergence of the dichotomy of causal and noncausal regions that is related to speed of propagation, in which the autonomous cognitive agents are not bound in a causal relationship with other agents. Arguments are presented for why the count of autonomous agents is best estimated with respect to the dimensionality of the underlying space. The number of autonomous agents obtained for the human brain equals twenty-five, and it is significant that the number in the sub-system modules also turns out to be close to the same value. It is possible that this near equality across layers provides a special uniqueness to the human brain. We argue that the findings of this study will be useful in the design of neural-network based AI systems that are designed to emulate human cognitive capacity. <br><br><br><br>

scholarly journals Number of autonomous agents in a neural network

2021 ◽  
Author(s):  
Subhash Kak
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Autonomous Agents ◽  
Size Structure ◽  
Cognitive Capacity ◽  
Scale Invariant ◽  
Cognitive Agents ◽  
Invariant Structure ◽  
Underlying Space ◽  
Speed Of Propagation

This paper considers several aspects of the relationship between size, structure, speed of propagation and the number of autonomous cognitive agents in a neural network. Whereas, memory and function generation capacities of neural networks with scale invariant structure have been investigated extensively, the number of autonomous agents has not received prior attention. We propose the emergence of the dichotomy of causal and noncausal regions that is related to speed of propagation, in which the autonomous cognitive agents are not bound in a causal relationship with other agents. Arguments are presented for why the count of autonomous agents is best estimated with respect to the dimensionality of the underlying space. The number of autonomous agents obtained for the human brain equals twenty-five, and it is significant that the number in the sub-system modules also turns out to be close to the same value. It is possible that this near equality across layers provides a special uniqueness to the human brain. We argue that the findings of this study will be useful in the design of neural-network based AI systems that are designed to emulate human cognitive capacity. <br><br><br><br>

The Inter-Human Cortex

2021 ◽  
pp. 102-112
Author(s):  
John Matthias
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Sound Art ◽  
Human Cortex ◽  
Biological Neural Network ◽  
Physical Network ◽  
Human Culture ◽  
Art Installation

This chapter outlines a theory of co-evolution of contexts and histories in human culture by making an analogy with the microscopic functionality of the human brain, and in particular Eugene Izhikevich’s idea of polychronization by mapping the network of ‘firing’ events in a biological neural network onto a network of ‘human events’ in the physical network of humans. The article utilizes the new theory to focus on the evolution of sound art by pointing to the multiplicity of origin contexts, and it examines a particular example of sound art installation, The Fragmented Orchestra (Jane Grant, John Matthias, and Nick Ryan) to exemplify the theory of the inter-human cortex.

scholarly journals SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data

2019 ◽  
Vol 36 (6) ◽  
pp. 1779-1784 ◽  
Author(s):  
Chuanqi Wang ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Count Data ◽  
Deep Neural Network ◽  
Sequencing Depth ◽  
Supplementary Information ◽  
Neural Network Classifier ◽  
Rna Seq ◽  
Scale Invariant ◽  
Downstream Analysis

Abstract Motivation Scaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly. Results We call an analysis method ‘scale-invariant’ (SI) if it gives the same result under different estimates of sequencing depth and hence can use the original count data without scaling. For the problem of classifying samples into pre-specified classes, such as normal versus cancerous, we develop a deep-neural-network based SI classifier named scale-invariant deep neural-network classifier (SINC). On nine bulk and single-cell datasets, the classification accuracy of SINC is better than or competitive to the best of eight other classifiers. SINC is easier to use and more reliable on data where proper sequencing depth is hard to determine. Availability and implementation This source code of SINC is available at https://www.nd.edu/∼jli9/SINC.zip. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Scale-invariance in the repeating fast radio burst 121102

2019 ◽  
Author(s):  
Hai-Nan Lin ◽  
Yu Sang
Keyword(s):  
Total Energy ◽  
Flux Density ◽  
Radio Burst ◽  
Probability Density Functions ◽  
Statistical Properties ◽  
Density Functions ◽  
Scale Invariant ◽  
Invariant Structure ◽  
Fast Radio Burst ◽  
Soft Gamma Repeater

Abstract The statistical properties of the repeating fast radio burst FRB 121102 are investigated. We find that the cumulative distributions of fluence, flux density, total energy and waiting time can be well fitted by the bent power law. In addition, the probability density functions of fluctuations of fluence, flux density and total energy well follow the Tsallis q-Gaussian distribution. The q values keep steady around q ∼ 2 for different scale intervals, indicating a scale-invariant structure of the bursts. The statistical properties of FRB 121102 are very similar to that of the soft gamma repeater SGR J1550-5418. The underlying physical implications need to be further investigated.

scholarly journals Insights into the Biology and Therapeutic Applications of Neural Stem Cells

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Lachlan Harris ◽  
Oressia Zalucki ◽  
Michael Piper ◽  
Julian Ik-Tsen Heng
Keyword(s):  
Stem Cells ◽  
Cerebral Cortex ◽  
Brain Injury ◽  
Neural Stem Cells ◽  
Human Brain ◽  
Therapeutic Potential ◽  
Biological Significance ◽  
Adult Brain ◽  
Cognitive Capacity ◽  
Molecular Characteristics

The cerebral cortex is essential for our higher cognitive functions and emotional reasoning. Arguably, this brain structure is the distinguishing feature of our species, and yet our remarkable cognitive capacity has seemingly come at a cost to the regenerative capacity of the human brain. Indeed, the capacity for regeneration and neurogenesis of the brains of vertebrates has declined over the course of evolution, from fish to rodents to primates. Nevertheless, recent evidence supporting the existence of neural stem cells (NSCs) in the adult human brain raises new questions about the biological significance of adult neurogenesis in relation to ageing and the possibility that such endogenous sources of NSCs might provide therapeutic options for the treatment of brain injury and disease. Here, we highlight recent insights and perspectives on NSCs within both the developing and adult cerebral cortex. Our review of NSCs during development focuses upon the diversity and therapeutic potential of these cells for use in cellular transplantation and in the modeling of neurodevelopmental disorders. Finally, we describe the cellular and molecular characteristics of NSCs within the adult brain and strategies to harness the therapeutic potential of these cell populations in the treatment of brain injury and disease.

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